My_KLSATD_Commits_and_my_Issues_from_Kafka.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "6OavZlOzf3Cb",
        "0Es76BXif_HV"
      ],
      "authorship_tag": "ABX9TyOCEWqyQSYwcin1QNBaqsWf",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/armandossrecife/my_validation/blob/main/My_KLSATD_Commits_and_my_Issues_from_Kafka.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Análise de ATD do Repositório do Kafka\n",
        "\n",
        "Dada uma lista de **classes críticas** identificadas pelo método ATDCodeAnalyzer (https://github.com/mining-software-repositories/cassandra), checa se estas classes estão relacionadas a commits dos issues com  architectural issues.\n",
        "\n",
        "A validação será feita por meio de métodos de SATD nos commits e nos issues do repositório.\n",
        "\n",
        "Logo abaixo seguem os passos da validação:\n",
        "\n",
        "1. Instala dependências e bibliotecas para execução dos scripts\n",
        "\n",
        "2. Funções e classes de apoio para análise de commits, issues e SATD\n",
        "\n",
        "3. Clona o repositório para análise\n",
        "\n",
        "4. Keywords de acordo com a literatura em SATD e KL SATD.\n",
        "\n",
        "5. Análise dos commits\n",
        "\n",
        "6. Análise dos issues\n",
        "\n",
        "7. Análise dos commits com SATD keywords e arquivos críticos e issues relacionados\n",
        "\n",
        "8. Validação dos arquivos críticos junto aos issues selecionados\n",
        "\n",
        "Obs: tempo de execução: ~1 hora"
      ],
      "metadata": {
        "id": "18X52AuBoN7k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Passos do experimento SATD em commits, issues com Classes Críticas (by ATDCodeAnalyzer)"
      ],
      "metadata": {
        "id": "I3MVPlieXk0L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Análise de SATD das mensagens e diffs de commits, bem como análise dos conteúdos de summary, description e comments de issues registrados em commits\n",
        "\n",
        "\n",
        "\n",
        "1.   Definir o conjunto de keywords da literatura e KL SATD\n",
        "\n",
        "2.   Fazer a análise de keywords para as mensagens dos commits e os diffs dos commits\n",
        "-  Filtrar apenas os commits que possuam as classes críticas (by ATDCodeAnalyzer)\n",
        "\n",
        "3.   Fazer a análise de keywords para os issues que são registrados nos commits para os summary, description e comments\n",
        "- Filtrar apenas os issues com commits que possuam as classes críticas (by ATDCodeAnalyzer)"
      ],
      "metadata": {
        "id": "U0wiDkA7Xo6l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## My SATD Analyzer\n",
        "\n",
        "Describe the steps to analyze commits and issues with SATD keywords"
      ],
      "metadata": {
        "id": "iu0j27gmX5Hh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Identify Keywords for Self-Admitted Technical Debt (SATD):\n",
        "Start by identifying keywords or phrases that commonly indicate self-admitted technical debt. These keywords can be derived from literature or based on your understanding of common terms used in code comments, commit messages, and issue descriptions. For example, common keywords may include \"TODO,\" \"FIXME,\" \"refactor,\" \"technical debt,\" \"hack,\" \"workaround,\" etc.\n",
        "\n",
        "2. Create a Dictionary of SATD Keywords:\n",
        "Build a dictionary or a set of SATD keywords that will serve as the basis for content queries in commit messages, modified file comments, and issue descriptions. This dictionary should include the keywords identified in Step 1.\n",
        "\n",
        "3. Using PyDriller for Commit Analysis:\n",
        "You can use PyDriller to analyze commits from the repository. PyDriller is a Python library for analyzing Git repositories. Install it using pip.\n",
        "Create Python scripts that use PyDriller to iterate through the commits and identify those that contain SATD keywords in their messages. You can filter commits based on these keywords and store them in a Set.\n",
        "\n",
        "4. Analyze Modified Files in Commits:\n",
        "For each commit, extract the list of modified files. Open and analyze these files to check for SATD keywords in the code comments within the modified lines. Store the commits with modified files containing SATD in a separate Set.\n",
        "\n",
        "5. Retrieve Issues from the Issue Tracker:\n",
        "Utilize an API or libraries like JIRA's REST API to fetch issues from the issue tracker (JIRA in this case). For example, you can use Python's requests library to interact with the JIRA API. Query issues based on project (e.g., CASSANDRA) and extract the content of the Summary, Description, and Comments fields for each issue."
      ],
      "metadata": {
        "id": "vD5aM62lX7-8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## My Validation"
      ],
      "metadata": {
        "id": "vs2CqECdx54W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As previously outlined, the ATDCodeAnalyzer approach introduced in this thesis aims to automatically identify source code artifacts affected by ATD. This approach distinguishes itself from previous methods by operating autonomously, obviating the requirement for expert analysis in software architecture within the context of the analyzed repository. Therefore, following the application of the ATDCodeAnalyzer, our objective is to determine the extent to which the identified critical files are genuinely influenced by architectural issues and technical debt. To achieve this, we have devised a qualitative validation method to assess the efficacy of our proposed approach.\n",
        "\n",
        "**A - Software Artifacts Collection and ATDCodeAnalyzer**: The process begins by extracting information from the repository and executing the ATDCodeAnalyzer to identify Critical Files based on our proposed approach.\n",
        "\n",
        "**B - Commit Analysis**: The modified files are analyzed based on the range and select only the commits that have critical files identified by ATDCodeAnalyzer.\n",
        "\n",
        "**C - Issue Tracker Analysis**: This phase is related to the process of extracting information from all issues from the issue tracker, recording information about fields related to each issue in the analyzed repository.\n",
        "\n",
        "**D - SATD Analysis**: This phase is related to extracting SATD keywords from commits in the repository. In this phase, it selects commits that have SATD keywords in messages and the diff of modified files\n",
        "\n",
        "**F - Select Issues for Inspection**: During this phase, issues are selected for manual inspection to check if these issues have architectural problems.\n",
        "\n",
        "**G - Inspection Aided by ChatGPT**: In this phase, we use ChatGPT to aid in the analysis of each issue to determine if the issue has architectural problems and save the justification for each inspection.\n",
        "\n",
        "**H - Issues with Architectural Problems**: Finally, in this last phase, we calculate the percentage of issues that do or do not have architectural problems to validate if the Critical files identified by ATDCodeAnalyzer have architectural issues.\n"
      ],
      "metadata": {
        "id": "RcU25l2mx9ct"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Horizontal Simple ATD Cassandra Analysis Commits, Issues, SATD and Architectural Issues (1).jpg](data:image/jpeg;base64,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)"
      ],
      "metadata": {
        "id": "DNIDKsisP6A9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Variáveis especiais"
      ],
      "metadata": {
        "id": "aukc88SSLE04"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Quantidade total de commits analisados faixa from x to y\n",
        "ve_lista_com_todos_os_commits = []\n",
        "ve_qtd_total_commits = 0\n",
        "\n",
        "#2. Quantidade de commits com arquivos críticos\n",
        "ve_lista_commits_com_arquivos_criticos = []\n",
        "ve_qtd_commits_com_arquivos_criticos = 0\n",
        "\n",
        "#3. Quantidade total de issues analisados\n",
        "ve_lista_com_todos_os_issues = []\n",
        "ve_qtd_total_issues = 0\n",
        "\n",
        "# 4. Correlação entre as métricas analisadas\n",
        "#4.1 Merge dos dados (commits e issues) dos arquivos críticos (8) - r4\n",
        "#4.2 Diagrama de Correlação (9) - r5\n",
        "\n",
        "#5. Quantidade total de issues\n",
        "ve_lista_com_todos_issues = []\n",
        "ve_qtd_total_issues = 0\n",
        "\n",
        "#5.1 Issues que aparecem nos commits\n",
        "ve_lista_com_issues_e_nos_commits = []\n",
        "ve_qtd_issues_e_nos_commits = 0\n",
        "\n",
        "#5.1 Issues que aparecem nos commits com arquivos críticos\n",
        "ve_lista_com_issues_e_nos_commits_com_arquivos_criticos = []\n",
        "ve_qtd_issues_e_nos_commits_com_arquivos_criticos = 0\n",
        "\n",
        "#6. Quantidade de commits que possuem SATD keywords\n",
        "#6.1 Commits com keywords\n",
        "ve_lista_com_todos_commits_com_keywords = []\n",
        "ve_qtd_total_commits_com_keywords = 0\n",
        "\n",
        "# 6.2 Commits com keywords e arquivos criticos\n",
        "ve_lista_com_commits_com_arquivos_criticos_com_keywords = []\n",
        "ve_qtd_commits_com_arquivos_criticos_com_keywords = 0"
      ],
      "metadata": {
        "id": "fZdJUiikLHXT"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Install the dependencies\n",
        "\n",
        "- Pydriller\n",
        "- GitPython\n",
        "- JiraPython\n",
        "- SQLite"
      ],
      "metadata": {
        "id": "bgbINqQeE2Wp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Install Pydriller.')\n",
        "!pip install pydriller > install_pydriller.log\n",
        "print('Install gitpython.')\n",
        "!pip3 install gitpython > install_gitpython.log\n",
        "print('Install Jira Python lib.')\n",
        "!pip install jira > install_jira_python.log\n",
        "print('Install SQLite in Linux')\n",
        "!sudo apt install -y sqlite3 > install_sqlite.log\n",
        "print('All depenpencies installed!')\n",
        "!cat *.log > install.log\n",
        "print('Details in install.log')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o75HXL4jYOYU",
        "outputId": "6b5c12f8-460c-49c9-996c-b55f7c487c57"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Install Pydriller.\n",
            "Install gitpython.\n",
            "Install Jira Python lib.\n",
            "Install SQLite in Linux\n",
            "\n",
            "WARNING: apt does not have a stable CLI interface. Use with caution in scripts.\n",
            "\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "All depenpencies installed!\n",
            "Details in install.log\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Import dependencies, configurations, functions and supporting classes"
      ],
      "metadata": {
        "id": "Ff3wGz-kYTfO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "8rFTG9t4BkSP"
      },
      "outputs": [],
      "source": [
        "from pydriller import Repository\n",
        "import tqdm\n",
        "import os\n",
        "import re\n",
        "\n",
        "from jira import JIRA\n",
        "from datetime import datetime, timedelta\n",
        "import sqlite3\n",
        "import subprocess\n",
        "\n",
        "import collections\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import random\n",
        "import scipy.stats as stats\n",
        "from sklearn.metrics import cohen_kappa_score\n",
        "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url_to_repository = 'https://github.com/apache/kafka.git'\n",
        "path_to_repository = 'kafka'\n",
        "os.environ['MY_REPOSITORY'] = url_to_repository\n",
        "\n",
        "JIRA_SERVER = 'https://issues.apache.org/jira'\n",
        "DATABASE_NAME = \"issues_db.db\"\n",
        "\n",
        "os.environ['DATABASE_NAME'] = DATABASE_NAME\n",
        "# Credentials\n",
        "os.environ['USERNAME'] = '?'\n",
        "os.environ['PASSWORD'] = '?'\n",
        "username = os.environ.get('USERNAME')\n",
        "password = os.environ.get('PASSWORD')"
      ],
      "metadata": {
        "id": "qe_FaWjSYoMu"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class SATDCommitAnalyzer:\n",
        "    def __init__(self, path_to_repository):\n",
        "        self.path_to_repository = path_to_repository\n",
        "\n",
        "    @staticmethod\n",
        "    def is_java_comment(line):\n",
        "        \"\"\"Checks if a line is a single-line Java comment.\n",
        "        Args:\n",
        "          line: The line to check.\n",
        "        Returns:\n",
        "          True if the line is a single-line Java comment, False otherwise.\n",
        "        \"\"\"\n",
        "        # Regular expression pattern to match Java comments\n",
        "        comment_pattern = r'^\\s*//|^\\s*/\\*|^\\s*\\*|^\\s*\\*/'\n",
        "        # Use the re.match function to check if the line matches the comment pattern\n",
        "        return bool(re.match(comment_pattern, line))\n",
        "\n",
        "    def analyze_commits_for_satd(self, start_date, end_date, satd_keywords):\n",
        "        \"\"\"Analyzes commits for SATD by checking if the commit message contains any of the specified SATD keywords.\n",
        "        Args:\n",
        "          start_date: The start date of the date range to analyze commits within.\n",
        "          end_date: The end date of the date range to analyze commits within.\n",
        "          satd_keywords: A list of SATD keywords to search for in commit messages.\n",
        "        Returns:\n",
        "          A tuple containing two sets:\n",
        "            * A set of commit hashes that contain SATD keywords in their commit messages.\n",
        "            * A dictionary mapping commit hashes to their associated commit messages.\n",
        "        \"\"\"\n",
        "        # Initialize sets to store commits and their associated SATD keywords\n",
        "        commits_with_satd = set()\n",
        "        dict_commit_msg = {}\n",
        "\n",
        "        # Traverse commits within the specified date range\n",
        "        print('Aguarde...')\n",
        "        my_traverser_commits = Repository(self.path_to_repository, since=start_date, to=end_date).traverse_commits()\n",
        "        total_commits = len(list(my_traverser_commits))\n",
        "\n",
        "        for commit in tqdm.tqdm(Repository(self.path_to_repository, since=start_date, to=end_date).traverse_commits(), total=total_commits, desc=\"Progress SATD message commit analysis\"):\n",
        "            for keyword in satd_keywords:\n",
        "                if keyword in commit.msg:\n",
        "                    commits_with_satd.add(commit.hash)\n",
        "                    dict_commit_msg[commit.hash] = commit.msg\n",
        "\n",
        "        return commits_with_satd, dict_commit_msg\n",
        "\n",
        "    def analyze_commit_diffs_for_satd(self, start_date, end_date, satd_keywords):\n",
        "        \"\"\"Analyzes commit diffs for SATD by checking if any of the specified SATD keywords are found in any of the added lines of the diff.\n",
        "        Args:\n",
        "          start_date: The start date of the date range to analyze commit diffs within.\n",
        "          end_date: The end date of the date range to analyze commit diffs within.\n",
        "          satd_keywords: A list of SATD keywords to search for in commit diffs.\n",
        "        Returns:\n",
        "          A dictionary mapping commit hashes to lists of tuples, where each tuple contains a SATD keyword and the corresponding line of the diff where the keyword was found.\n",
        "        \"\"\"\n",
        "        # Initialize a dictionary to store commit hashes and their associated SATD keywords and diff content\n",
        "        dict_commit_diffs = {}\n",
        "\n",
        "        # Traverse commits within the specified date range\n",
        "        print('Aguarde...')\n",
        "        my_traverser_commits = Repository(self.path_to_repository, since=start_date, to=end_date).traverse_commits()\n",
        "        total_commits = len(list(my_traverser_commits))\n",
        "\n",
        "        for commit in tqdm.tqdm(Repository(self.path_to_repository, since=start_date, to=end_date).traverse_commits(), total=total_commits, desc=\"Progress SATD diff commit analysis\"):\n",
        "            list_keywords_by_commit = []\n",
        "            list_diff_content_by_commit = []\n",
        "\n",
        "            for modification in commit.modified_files:\n",
        "                for line in modification.diff_parsed['added']:\n",
        "                    valor_linha = line[0]\n",
        "                    conteudo_linha = line[1]\n",
        "\n",
        "                    for keyword in satd_keywords:\n",
        "                        if SATDCommitAnalyzer.is_java_comment(conteudo_linha):\n",
        "                            if keyword in conteudo_linha:\n",
        "                                list_keywords_by_commit.append(keyword)\n",
        "                                list_diff_content_by_commit.append(conteudo_linha)\n",
        "\n",
        "            if list_keywords_by_commit:\n",
        "                elemento = list_keywords_by_commit, list_diff_content_by_commit\n",
        "                dict_commit_diffs[commit.hash] = elemento\n",
        "\n",
        "        return dict_commit_diffs\n",
        "\n",
        "    def total_of_commits(self, start_date, end_date):\n",
        "        # Traverse commits within the specified date range\n",
        "        print('Aguarde...')\n",
        "        my_traverser_commits = Repository(self.path_to_repository, since=start_date, to=end_date).traverse_commits()\n",
        "        total_commits = len(list(my_traverser_commits))\n",
        "        lista_hash_commits = []\n",
        "        for commit in tqdm.tqdm(Repository(self.path_to_repository, since=start_date, to=end_date).traverse_commits(), total=total_commits, desc=\"Progress messagem commit analysis\"):\n",
        "            lista_hash_commits.append(commit.hash)\n",
        "        return total_commits, lista_hash_commits"
      ],
      "metadata": {
        "id": "TulocN-hYuNs"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_commits_by_range_and_critical_files(initial_date, final_date, critical_files):\n",
        "  \"\"\"Extracts information from a date range of commits, focusing on critical files.\n",
        "  Args:\n",
        "      initial_date: inicial date of commits\n",
        "      final_date: final date of commits\n",
        "      critical_files: A list of critical file paths. ex: ['StorageService.java', 'ColumnFamilyStore.java']\n",
        "  Returns:\n",
        "        A dictionary of commits, where the keys are the commit hashes and the\n",
        "        values are tuples containing the commit message, a list of critical files modified in the commit, a dictionary of diff [filename]:filename.diff, a list_of_modified_files, a list of all diffs from modified files\n",
        "  \"\"\"\n",
        "\n",
        "  dict_commit_modified_files = {}\n",
        "  print('Wait...')\n",
        "  my_traverser_commits = Repository(\"kafka\", since=initial_date, to=final_date).traverse_commits()\n",
        "  total_commits = len(list(my_traverser_commits))\n",
        "  try:\n",
        "    for commit in tqdm.tqdm(Repository(\"kafka\", since=initial_date, to=final_date).traverse_commits(), total=total_commits, desc=\"Progress critical files commit anlysis\"):\n",
        "      list_of_critical_files_modified = []\n",
        "      list_of_modified_files = []\n",
        "      list_dict_of_diff_files = []\n",
        "      list_dict_of_diff_modified_files = []\n",
        "      for m in commit.modified_files:\n",
        "        dict_diff_file = {}\n",
        "        list_of_modified_files.append(m.filename)\n",
        "        list_dict_of_diff_modified_files.append(m.diff)\n",
        "        if m.filename in critical_files:\n",
        "          list_of_critical_files_modified.append(m.filename)\n",
        "          dict_diff_file[m.filename] = m.diff\n",
        "          list_dict_of_diff_files.append(dict_diff_file)\n",
        "      if len(list_of_critical_files_modified) > 0:\n",
        "        data_commit = str(commit.committer_date.day) + '/' + str(commit.committer_date.month) + '/' + str(commit.committer_date.year)\n",
        "        element = commit.msg, list_of_critical_files_modified, data_commit, commit.lines, commit.files, list_dict_of_diff_files, list_of_modified_files, list_dict_of_diff_modified_files\n",
        "        dict_commit_modified_files[commit.hash] = element\n",
        "  except Exception as ex:\n",
        "    print(f'Erro during travesse commits: {str(ex)}')\n",
        "  return dict_commit_modified_files, total_commits\n",
        "\n",
        "def get_commits_by_range(initial_date, final_date):\n",
        "  \"\"\"Extracts information from a date range of commits\n",
        "  Args:\n",
        "      initial_date: inicial date of commits\n",
        "      final_date: final date of commits\n",
        "  Returns:\n",
        "        A dictionary of commits, where the keys are the commit hashes and the\n",
        "        values are tuples containing the commit message, files modified in the commit, a dictionary of diff [filename]:filename.diff, a list_of_modified_files, a list of all diffs from modified files\n",
        "  \"\"\"\n",
        "  dict_commit_modified_files = {}\n",
        "  print('Wait...')\n",
        "  my_traverser_commits = Repository(\"kafka\", since=initial_date, to=final_date).traverse_commits()\n",
        "  total_commits = len(list(my_traverser_commits))\n",
        "  try:\n",
        "    for commit in tqdm.tqdm(Repository(\"kafka\", since=initial_date, to=final_date).traverse_commits(), total=total_commits, desc=\"Progress commit anlysis\"):\n",
        "\n",
        "      list_of_modified_files = []\n",
        "      list_dict_of_diff_files = []\n",
        "      list_dict_of_diff_modified_files = []\n",
        "      for m in commit.modified_files:\n",
        "        if m is not None:\n",
        "          dict_diff_file = {}\n",
        "          list_of_modified_files.append(m.filename)\n",
        "          list_dict_of_diff_modified_files.append(m.diff)\n",
        "      if len(list_of_modified_files) > 0:\n",
        "        data_commit = str(commit.committer_date.day) + '/' + str(commit.committer_date.month) + '/' + str(commit.committer_date.year)\n",
        "        element = commit.msg, data_commit, commit.lines, commit.files, list_dict_of_diff_files, list_of_modified_files, list_dict_of_diff_modified_files\n",
        "        dict_commit_modified_files[commit.hash] = element\n",
        "  except Exception as ex:\n",
        "    print(f'Erro during travesse commits: {str(ex)}')\n",
        "  return dict_commit_modified_files, total_commits"
      ],
      "metadata": {
        "id": "TcWPN-WXYxqU"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class JiraIssue:\n",
        "  def __init__(self, key, summary, issue_type, status, priority, description, comments):\n",
        "    self.key = key\n",
        "    self.summary = summary\n",
        "    self.issue_type = issue_type\n",
        "    self.status = status\n",
        "    self.priority = priority\n",
        "    self.description = description\n",
        "    self.comments = comments\n",
        "\n",
        "  def get_comments(self):\n",
        "    return self.comments\n",
        "\n",
        "  def __str__(self):\n",
        "    return (f'Key: {self.key}, Summary: {self.summary}, Type: {self.issue_type}, Status: {self.status}')\n",
        "\n",
        "class JiraIssues:\n",
        "  def __init__(self,project, issues):\n",
        "    self.project = project\n",
        "    self.issues = issues\n",
        "\n",
        "  def add_issue(self, issue):\n",
        "    self.issues.append(issue)\n",
        "\n",
        "  def get_issues(self) -> list:\n",
        "    return self.issues\n",
        "\n",
        "  def update_issues(self, issues):\n",
        "    self.issues = issues\n",
        "\n",
        "  def __str__(self):\n",
        "    str_issues = \"\"\n",
        "    for issue in self.get_issues():\n",
        "      str_issues = str_issues + str(issue)\n",
        "      str_issues = str_issues + ', '\n",
        "    str_issues = '[' + str_issues + ']'\n",
        "    return (f'Project: {self.project}, Qdt of issues: {len(self.issues)}, Issues: {str_issues}')\n",
        "\n",
        "# Classe de utilidades para manipular o servidor Jira\n",
        "class JiraUtils:\n",
        "  def __init__(self, project, jira_instance):\n",
        "    self.project = project\n",
        "    self.jira_jira_instance = jira_instance\n",
        "\n",
        "  def generate_intervals_between_dates(self, date1: tuple, date2: tuple, distance=120) -> list:\n",
        "    start_date = datetime(date1[0], date1[1], date1[2])\n",
        "    end_date = datetime(date2[0], date2[1], date2[2])\n",
        "    interval_days = distance\n",
        "    # Initialize a list to store the intervals\n",
        "    intervals = []\n",
        "    # Initialize the current date as the start date\n",
        "    current_date = start_date\n",
        "    # Loop to generate intervals until the current date is less than or equal to the end date\n",
        "    while current_date < end_date:\n",
        "        interval = (current_date, current_date + timedelta(days=interval_days - 1))\n",
        "        intervals.append(interval)\n",
        "        current_date += timedelta(days=interval_days)\n",
        "    return intervals\n",
        "\n",
        "  def convert_interval_dates(self, dates: list) -> list:\n",
        "    list_interval_dates = []\n",
        "    for each in dates:\n",
        "      date1 = each[0]\n",
        "      # Convert the date to a string in the format \"YYYY/MM/DD\".\n",
        "      str_date1 = date1.strftime(\"%Y/%m/%d\")\n",
        "      date2 = each[1]\n",
        "      str_date2 = date2.strftime(\"%Y/%m/%d\")\n",
        "      elemento = str_date1, str_date2\n",
        "      list_interval_dates.append(elemento)\n",
        "    return list_interval_dates\n",
        "\n",
        "  def generate_list_of_sentences(self, dates: list) -> list:\n",
        "    lista_sentencas = []\n",
        "    for each in dates:\n",
        "      str_date1 = each[0].strftime(\"%Y/%m/%d\")\n",
        "      str_date2 = each[1].strftime(\"%Y/%m/%d\")\n",
        "      sentenca = f'project={self.project.upper()} and created>=\"{str_date1}\" and created<=\"{str_date2}\"'\n",
        "      lista_sentencas.append(sentenca)\n",
        "    return lista_sentencas\n",
        "\n",
        "  def get_list_of_block_issues_by_dates(self,date1, date2, distance=120) -> list:\n",
        "    print('Aguarde...')\n",
        "    t1 = datetime.now()\n",
        "    list_of_dates = self.generate_intervals_between_dates(date1,date2,distance)\n",
        "    lista_sentencas = self.generate_list_of_sentences(list_of_dates)\n",
        "    lista_bloco_issues_by_date = []\n",
        "    total_items = len(lista_sentencas)\n",
        "    i = 0\n",
        "    iterable_lista_sentencas = tqdm.tqdm(lista_sentencas, total=total_items)\n",
        "    for each in iterable_lista_sentencas:\n",
        "      issues_by_date_temp = self.jira_jira_instance.search_issues(each,maxResults=1000)\n",
        "      print(f'Range: {each}, qtd issues: {len(issues_by_date_temp)}')\n",
        "      lista_bloco_issues_by_date.append(issues_by_date_temp)\n",
        "      percentage = (i + 1) / total_items * 100\n",
        "      iterable_lista_sentencas.set_description(f\"Progress Message Analysis\")\n",
        "    i += 1\n",
        "    t2 = datetime.now()\n",
        "    print(t2)\n",
        "    print(f'Tempo da consulta: {t2-t1}')\n",
        "    return lista_bloco_issues_by_date\n",
        "\n",
        "  def concatenate_block_of_issues(self,block_of_issues):\n",
        "    concatenated_list = [item for sublist in block_of_issues for item in sublist]\n",
        "    print(f'Total de issues recuperados: {len(concatenated_list)}')\n",
        "    return concatenated_list\n",
        "\n",
        "class IssuesDatabase:\n",
        "    def __init__(self, database_name):\n",
        "        self.database_name = database_name\n",
        "        self.create_tables()\n",
        "\n",
        "    def create_tables(self):\n",
        "        self.conn = sqlite3.connect(self.database_name)\n",
        "        self.cursor = self.conn.cursor()\n",
        "\n",
        "        self.cursor.execute('''\n",
        "            CREATE TABLE IF NOT EXISTS Issues (\n",
        "                id INTEGER PRIMARY KEY,\n",
        "                project TEXT,\n",
        "                key TEXT,\n",
        "                summary TEXT,\n",
        "                issue_type TEXT,\n",
        "                status TEXT,\n",
        "                priority TEXT,\n",
        "                description TEXT\n",
        "            )\n",
        "        ''')\n",
        "\n",
        "        self.cursor.execute('''\n",
        "            CREATE TABLE IF NOT EXISTS Comments (\n",
        "                id INTEGER PRIMARY KEY,\n",
        "                key TEXT,\n",
        "                comment TEXT\n",
        "            )\n",
        "        ''')\n",
        "\n",
        "        self.conn.commit()\n",
        "\n",
        "    def insert_in_table_issues(self, project, key, summary, issue_type, status, priority, description):\n",
        "        values = (None, project, key, summary, issue_type, status, priority, description)\n",
        "        self.cursor.execute('''\n",
        "            INSERT INTO Issues\n",
        "            (id, project, key, summary, issue_type, status, priority, description)\n",
        "            VALUES (?, ?, ?, ?, ?, ?, ?, ?)\n",
        "        ''', values)\n",
        "\n",
        "        self.conn.commit()\n",
        "\n",
        "    def insert_in_table_comments(self, key, comment):\n",
        "        values = (None, key, comment)\n",
        "        self.cursor.execute('''\n",
        "            INSERT INTO Comments\n",
        "            (id, key, comment)\n",
        "            VALUES (?, ?, ?)\n",
        "        ''', values)\n",
        "\n",
        "        self.conn.commit()\n",
        "\n",
        "    def show_content(self, table):\n",
        "        query = f\"SELECT * FROM {table}\"\n",
        "        self.cursor.execute(query)\n",
        "\n",
        "        rows = self.cursor.fetchall()\n",
        "        for row in rows:\n",
        "            print(row)\n",
        "\n",
        "    def show_n_lines(self, table, n):\n",
        "        query = f\"SELECT * FROM {table}\"\n",
        "        self.cursor.execute(query)\n",
        "\n",
        "        rows = self.cursor.fetchall()\n",
        "\n",
        "        for i, row in enumerate(rows):\n",
        "            print(row)\n",
        "            if i == n:\n",
        "              break\n",
        "\n",
        "    def close_connection(self):\n",
        "        self.conn.close()\n",
        "\n",
        "def analyze_jira_all_issues(project, all_issues):\n",
        "    # Create an instance of JiraIssues to manage all issues\n",
        "    my_all_issues = JiraIssues(project, [])\n",
        "\n",
        "    total_items = len(all_issues)\n",
        "\n",
        "    # Iterate through the fetched issues\n",
        "    for issue in tqdm.tqdm(all_issues, total=total_items, desc='Progress jira all issues analysis'):\n",
        "        issue_key = issue.key\n",
        "        issue_summary = issue.fields.summary\n",
        "        issue_description = issue.fields.description\n",
        "        issue_comments = [comment.body for comment in issue.fields.comment.comments]\n",
        "\n",
        "        # Check for SATD keywords in the issue's summary, description, and comments\n",
        "        if issue_summary is None:\n",
        "            issue_summary = \"\"\n",
        "        if issue_description is None:\n",
        "            issue_description = \"\"\n",
        "        if issue_comments is None:\n",
        "            issue_comments = \"\"\n",
        "\n",
        "        #is_satd = any(keyword in (issue_summary + issue_description + ' '.join(issue_comments)) for keyword in satd_keywords)\n",
        "\n",
        "        #if is_satd:\n",
        "        issue_type = issue.fields.issuetype.name\n",
        "        issue_status = issue.fields.status.name\n",
        "        issue_priority = issue.fields.priority.name\n",
        "\n",
        "        # Create a JiraIssue instance for the SATD issue\n",
        "        my_issue = JiraIssue(issue_key, issue_summary, issue_type, issue_status, issue_priority, issue_description, issue_comments)\n",
        "\n",
        "            # Add the SATD issue to the list of SATD issues\n",
        "        my_all_issues.add_issue(my_issue)\n",
        "\n",
        "    return my_all_issues"
      ],
      "metadata": {
        "id": "MpSG5TwXY2y0"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!rm -rf kafka"
      ],
      "metadata": {
        "id": "H347Ba-SoxYl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Cloning the local repository"
      ],
      "metadata": {
        "id": "jAgpa6ckZR43"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Clona o repositório : {url_to_repository}')\n",
        "!git clone $MY_REPOSITORY"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0J88HDPkZVEo",
        "outputId": "4c065a24-94c2-4919-fc9f-fbc69b26d7cd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Clona o repositório : https://github.com/apache/kafka.git\n",
            "Cloning into 'kafka'...\n",
            "remote: Enumerating objects: 355073, done.\u001b[K\n",
            "remote: Counting objects: 100% (1/1), done.\u001b[K\n",
            "remote: Total 355073 (delta 0), reused 1 (delta 0), pack-reused 355072\u001b[K\n",
            "Receiving objects: 100% (355073/355073), 166.19 MiB | 21.64 MiB/s, done.\n",
            "Resolving deltas: 100% (171246/171246), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cd kafka && git log --pretty=\"%H %s\" > all_commits_msg.txt\n",
        "!cd kafka && git log --pretty=\"%H;%ai;%s\" > all_commits_full.txt\n",
        "\n",
        "# Dados do 1o commit e do ultimo commit\n",
        "!echo \"Último commit: \"\n",
        "!head /content/kafka/all_commits_full.txt -n 1\n",
        "!echo \"Primeiro commit: \"\n",
        "!tail /content/kafka/all_commits_full.txt -n 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SC1pl1zuZZVi",
        "outputId": "6a264183-086c-45cd-f53a-eeabeb236198"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Último commit: \n",
            "49d3122d425171b6a59a2b6f02d3fe63d3ac2397;2023-11-13 10:15:35 +0100;MINOR: Fix ClusterConnectionStatesTest.testSingleIP (#14741)\n",
            "Primeiro commit: \n",
            "642da2f28c9bc6e373603d6d9119ce33684090f5;2011-08-01 23:41:24 +0000;Initial checkin of Kafka to Apache SVN. This corresponds to https://github.com/kafka-dev/kafka/commit/709afe4ec75489bc00a44335de8821fa726bb97e except that git specific files have been removed and code has been put into trunk/branches/site/etc. This is just a copy of master, branches and history are not being converted since we can't find a good tool for it.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Keywords about SATD (Self-admitted Technical Debt)\n",
        "\n",
        "Self-Admitted Technical Debt (SATD) refers to situations where the developers are aware that the current implementation is not optimal and write comments alerting of the problems [3]"
      ],
      "metadata": {
        "id": "srvSJFSDZqCi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[1] Rantala, Leevi, and Mika Mäntylä. \"Predicting technical debt from commit contents: reproduction and extension with automated feature selection.\" Software Quality Journal 28 (2020): 1551-1579. ~> https://link.springer.com/article/10.1007/s11219-020-09520-3\n",
        "\n",
        "[2] Li, Yikun, Mohamed Soliman, and Paris Avgeriou. \"Automatic identification of self-admitted technical debt from four different sources.\" Empirical Software Engineering 28, no. 3 (2023): 1-38. ~> https://link.springer.com/article/10.1007/s10664-023-10297-9\n",
        "\n",
        "[3] Potdar, Aniket, and Emad Shihab. \"An exploratory study on self-admitted technical debt.\" In 2014 IEEE International Conference on Software Maintenance and Evolution, pp. 91-100. IEEE, 2014. ~> https://ieeexplore.ieee.org/abstract/document/6976075"
      ],
      "metadata": {
        "id": "aNKL4dNTZttg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "satd_keywords_1_2 = {\n",
        "    \"typo\", \"unused import\", \"error message\", \"comment\", \"logging\", \"javadoc\", \"minor\", \"update\",\n",
        "    \"debug\", \"code cleanup\", \"formatting\", \"more tests\", \"documentation\", \"work in progress\",\n",
        "    \"improvement\", \"rename\", \"support for\", \"header\", \"interface\", \"annotation\", \"naming\",\n",
        "    \"tidy up\", \"files\", \"extension point\", \"exception\", \"handling\", \"test\", \"output\", \"cast\",\n",
        "    \"simplify\", \"findbugs\", \"leak\", \"implementation\", \"unused code\", \"API\", \"refactoring\",\n",
        "    \"checkstyle errors\", \"redundant\", \"deprecated code\", \"constructor\", \"endpoints\", \"flaky\",\n",
        "    \"unused\", \"unnecessary\", \"confusing\", \"ugly\", \"simplify\", \"too much\", \"not used\", \"more readable\",\n",
        "    \"more efficient\", \"dead code\", \"infinite loop\", \"too long\", \"not implemented\", \"less verbose\",\n",
        "    \"more robust\", \"speed up\", \"get rid of\", \"not thread safe\", \"clean up code\", \"not done yet\",\n",
        "    \"avoid extra seek\", \"reduce duplicate code\", \"no longer needed\", \"not supported yet\",\n",
        "    \"documentation doesn't match\", \"short term solution\", \"spurious error messages\", \"it'd be nice\",\n",
        "    \"please add a test\", \"would significantly improve\", \"performance\", \"makes it much easier\",\n",
        "    \"avoid calling it twice\", \"takes a long time\", \"good to have coverage\",\n",
        "    \"makes it very hard\", \"patch doesn't apply cleanly\", \"it's not perfectly documented\",\n",
        "    \"need to update documentation\", \"make it less brittle\", \"documentation does not mention\",\n",
        "    \"wastes a lot of space\", \"there is no unit test\", \"lead to huge memory allocation\",\n",
        "    \"test doesn't add much value\", \"some holes in the doc\", \"by hard coding instead of\",\n",
        "    \"should be updated to reflect\", \"more tightly coupled than ideal\", \"any chance of a test\",\n",
        "    \"should improve a bit by\", \"solution won't be really satisfactory\", \"misleading\",\n",
        "    \"too long\", \"please add a test\", \"there is no unit test\", \"any chance of a test\",\n",
        "    \"good to have coverage\", \"test doesn't add much value\", \"flaky\",\n",
        "}\n",
        "\n",
        "satd_keywords_diff_3 = {'TODO', 'FIXME', 'refactor', 'refact', 'hack', 'workaround', 'technical debt', 'cleanup', 'clean', 'fix'}\n",
        "\n",
        "# Extra keywords\n",
        "extra_keywords = set([\n",
        "    \"architectural issue\", \"architectural smell\", \"architectural debt\", \"design debt\", \"code rot\", \"spaghetti code\",\n",
        "    \"quick fix\", \"temporary solution\", \"workaround\", \"hack\", \"technical kludge\", \"inconsistency\", \"race condition\", \"performance\",\n",
        "    \"scalability\", \"reliability\", \"maintainability\", \"code duplication\", \"code complexity\", \"tight coupling\", \"fragile code\",\n",
        "    \"magic numbers\", \"magic strings\", \"lack of modularity\", \"lack of encapsulation\", \"lack of abstraction\", \"lack of documentation\",\n",
        "    \"lack of testing\", \"complexity\", \"duplication\", \"modularity\", \"encapsulation\", \"design\", \"violation\", \"dependency\",\n",
        "    \"dependencies\", \"refactoring\", \"cyclic dependency\", \"unstable\", \"cross-module\",\n",
        "    \"code smell\", \"architectural problem\", \"design issue\", \"system design problem\",\n",
        "    \"technical debt due to architectural issues\", \"code debt\", \"technical debt due to design issues\", \"design flaws\",\n",
        "    \"disorganized code\", \"difficult to maintain code\", \"poorly structured code\", \"complex code\",\n",
        "    \"concurrency issue\", \"multithreading issue\", \"synchronization issue\",\n",
        "    \"speed\", \"throughput\", \"latency\", \"response time\",\n",
        "    \"scalability issue\", \"ability to handle increased load\",\n",
        "    \"dependability\", \"trustworthiness\", \"fault tolerance\", \"robustness\",\n",
        "    \"ease of maintenance\", \"maintainability issue\", \"ability to evolve\",\n",
        "    \"code redundancy\", \"repeated code\",\n",
        "    \"code difficulty\", \"complex code\", \"difficult to understand code\",\n",
        "    \"code entanglement\", \"code interdependencies\", \"complex code relationships\",\n",
        "    \"delicate code\", \"brittle code\", \"easy to break code\",\n",
        "    \"literal values\", \"hard-coded values\", \"undocumented values\",\n",
        "    \"literal strings\", \"hard-coded strings\", \"undocumented strings\",\n",
        "    \"monolithic code\", \"lack of separation of concerns\", \"lack of cohesion\",\n",
        "    \"exposed internal state\", \"lack of information hiding\",\n",
        "    \"concrete code\", \"lack of generalization\",\n",
        "    \"undocumented code\", \"poorly documented code\", \"lack of code comments\",\n",
        "    \"untested code\", \"poorly tested code\", \"lack of test cases\",\n",
        "    \"code complexity\", \"cyclomatic complexity\", \"cognitive complexity\",\n",
        "    \"code dependencies\", \"module dependencies\", \"system dependencies\",\n",
        "    \"code restructuring\", \"code improvement\", \"code simplification\",\n",
        "    \"circular dependency\", \"indirect dependency\",\n",
        "    \"buggy code\", \"flaky code\", \"unreliable code\",\n",
        "    \"inter-module\", \"module-to-module\"\n",
        "])\n",
        "\n",
        "extra_keywords_td = set([\n",
        "    \"anti-pattern\", \"bad practice\", \"code smell\", \"design smell\", \"technical debt\",\n",
        "    \"wrong solution\", \"poor solution\", \"ineffective solution\", \"inefficient solution\", \"suboptimal solution\",\n",
        "    \"wrong way\", \"poor way\", \"ineffective way\", \"inefficient way\", \"suboptimal way\",\n",
        "    \"code defect\", \"code problem\", \"code issue\", \"code flaw\",\n",
        "    \"design defect\", \"design problem\", \"design issue\", \"design flaw\",\n",
        "    \"code debt\", \"design debt\", \"architectural debt\", \"implementation debt\", \"testing debt\", \"documentation debt\"\n",
        "])\n",
        "\n",
        "my_satd_keywords = satd_keywords_diff_3.union(satd_keywords_1_2)\n",
        "\n",
        "my_satd_aux = my_satd_keywords.union(extra_keywords)\n",
        "\n",
        "final_satd_keywords = my_satd_aux.union(extra_keywords_td)\n",
        "\n",
        "# Sort the set of strings in ascending order.\n",
        "sorted_my_satd_keywords = sorted(final_satd_keywords)\n",
        "\n",
        "print(f'Qtd de sorted_my_satd_keywords: {len(sorted_my_satd_keywords)}')\n",
        "# Print the sorted set of strings.\n",
        "for word in sorted_my_satd_keywords:\n",
        "  print(word)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGH3QKQlZ1ju",
        "outputId": "f9fa8d8e-f02b-4380-c97d-ddc677d4d214"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Qtd de sorted_my_satd_keywords: 235\n",
            "API\n",
            "FIXME\n",
            "TODO\n",
            "ability to evolve\n",
            "ability to handle increased load\n",
            "annotation\n",
            "anti-pattern\n",
            "any chance of a test\n",
            "architectural debt\n",
            "architectural issue\n",
            "architectural problem\n",
            "architectural smell\n",
            "avoid calling it twice\n",
            "avoid extra seek\n",
            "bad practice\n",
            "brittle code\n",
            "buggy code\n",
            "by hard coding instead of\n",
            "cast\n",
            "checkstyle errors\n",
            "circular dependency\n",
            "clean\n",
            "clean up code\n",
            "cleanup\n",
            "code cleanup\n",
            "code complexity\n",
            "code debt\n",
            "code defect\n",
            "code dependencies\n",
            "code difficulty\n",
            "code duplication\n",
            "code entanglement\n",
            "code flaw\n",
            "code improvement\n",
            "code interdependencies\n",
            "code issue\n",
            "code problem\n",
            "code redundancy\n",
            "code restructuring\n",
            "code rot\n",
            "code simplification\n",
            "code smell\n",
            "cognitive complexity\n",
            "comment\n",
            "complex code\n",
            "complex code relationships\n",
            "complexity\n",
            "concrete code\n",
            "concurrency issue\n",
            "confusing\n",
            "constructor\n",
            "cross-module\n",
            "cyclic dependency\n",
            "cyclomatic complexity\n",
            "dead code\n",
            "debug\n",
            "delicate code\n",
            "dependability\n",
            "dependencies\n",
            "dependency\n",
            "deprecated code\n",
            "design\n",
            "design debt\n",
            "design defect\n",
            "design flaw\n",
            "design flaws\n",
            "design issue\n",
            "design problem\n",
            "design smell\n",
            "difficult to maintain code\n",
            "difficult to understand code\n",
            "disorganized code\n",
            "documentation\n",
            "documentation debt\n",
            "documentation does not mention\n",
            "documentation doesn't match\n",
            "duplication\n",
            "ease of maintenance\n",
            "easy to break code\n",
            "encapsulation\n",
            "endpoints\n",
            "error message\n",
            "exception\n",
            "exposed internal state\n",
            "extension point\n",
            "fault tolerance\n",
            "files\n",
            "findbugs\n",
            "fix\n",
            "flaky\n",
            "flaky code\n",
            "formatting\n",
            "fragile code\n",
            "get rid of\n",
            "good to have coverage\n",
            "hack\n",
            "handling\n",
            "hard-coded strings\n",
            "hard-coded values\n",
            "header\n",
            "implementation\n",
            "implementation debt\n",
            "improvement\n",
            "inconsistency\n",
            "indirect dependency\n",
            "ineffective solution\n",
            "ineffective way\n",
            "inefficient solution\n",
            "inefficient way\n",
            "infinite loop\n",
            "inter-module\n",
            "interface\n",
            "it'd be nice\n",
            "it's not perfectly documented\n",
            "javadoc\n",
            "lack of abstraction\n",
            "lack of code comments\n",
            "lack of cohesion\n",
            "lack of documentation\n",
            "lack of encapsulation\n",
            "lack of generalization\n",
            "lack of information hiding\n",
            "lack of modularity\n",
            "lack of separation of concerns\n",
            "lack of test cases\n",
            "lack of testing\n",
            "latency\n",
            "lead to huge memory allocation\n",
            "leak\n",
            "less verbose\n",
            "literal strings\n",
            "literal values\n",
            "logging\n",
            "magic numbers\n",
            "magic strings\n",
            "maintainability\n",
            "maintainability issue\n",
            "make it less brittle\n",
            "makes it much easier\n",
            "makes it very hard\n",
            "minor\n",
            "misleading\n",
            "modularity\n",
            "module dependencies\n",
            "module-to-module\n",
            "monolithic code\n",
            "more efficient\n",
            "more readable\n",
            "more robust\n",
            "more tests\n",
            "more tightly coupled than ideal\n",
            "multithreading issue\n",
            "naming\n",
            "need to update documentation\n",
            "no longer needed\n",
            "not done yet\n",
            "not implemented\n",
            "not supported yet\n",
            "not thread safe\n",
            "not used\n",
            "output\n",
            "patch doesn't apply cleanly\n",
            "performance\n",
            "please add a test\n",
            "poor solution\n",
            "poor way\n",
            "poorly documented code\n",
            "poorly structured code\n",
            "poorly tested code\n",
            "quick fix\n",
            "race condition\n",
            "reduce duplicate code\n",
            "redundant\n",
            "refact\n",
            "refactor\n",
            "refactoring\n",
            "reliability\n",
            "rename\n",
            "repeated code\n",
            "response time\n",
            "robustness\n",
            "scalability\n",
            "scalability issue\n",
            "short term solution\n",
            "should be updated to reflect\n",
            "should improve a bit by\n",
            "simplify\n",
            "solution won't be really satisfactory\n",
            "some holes in the doc\n",
            "spaghetti code\n",
            "speed\n",
            "speed up\n",
            "spurious error messages\n",
            "suboptimal solution\n",
            "suboptimal way\n",
            "support for\n",
            "synchronization issue\n",
            "system dependencies\n",
            "system design problem\n",
            "takes a long time\n",
            "technical debt\n",
            "technical debt due to architectural issues\n",
            "technical debt due to design issues\n",
            "technical kludge\n",
            "temporary solution\n",
            "test\n",
            "test doesn't add much value\n",
            "testing debt\n",
            "there is no unit test\n",
            "throughput\n",
            "tidy up\n",
            "tight coupling\n",
            "too long\n",
            "too much\n",
            "trustworthiness\n",
            "typo\n",
            "ugly\n",
            "undocumented code\n",
            "undocumented strings\n",
            "undocumented values\n",
            "unnecessary\n",
            "unreliable code\n",
            "unstable\n",
            "untested code\n",
            "unused\n",
            "unused code\n",
            "unused import\n",
            "update\n",
            "violation\n",
            "wastes a lot of space\n",
            "work in progress\n",
            "workaround\n",
            "would significantly improve\n",
            "wrong solution\n",
            "wrong way\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KL-SATD\n",
        "\n",
        "A specific subtype of SATD comments is Keyword-Labeled SATD comments (KL-SATD). These are defined as SATD comments, which have a SATD-related keyword in them such as “TODO”, or “FIXME” [8]\n",
        "\n",
        "[8] L. Rantala, M. M ̈antyl ̈a, D. Lo, Prevalence, contents and automatic detection of kl-satd, in: 2020 46th Euromicro Conference on Software Engineer- ing and Advanced Applications (SEAA).\n"
      ],
      "metadata": {
        "id": "xJPcJEjbLlC4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kl_satd_keywords = ['TODO', 'FIXME', 'FIX']\n",
        "\n",
        "final_kl_satd_keywords = set(kl_satd_keywords)\n",
        "print(final_kl_satd_keywords)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rKI5yW0tLtBU",
        "outputId": "d21a3552-2b55-4169-e694-ad2977f48f5b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'TODO', 'FIX', 'FIXME'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Analyze commits\n",
        "\n",
        "Analisa todas as mensagens e diffs dos commits entre 2/03/2009 e 4/10/2023 do Cassandra que contem as Keywords coletadas da literatura."
      ],
      "metadata": {
        "id": "ji-77s6LaF2x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Análise de todos os commits de uma faixa"
      ],
      "metadata": {
        "id": "zHOnpqyQW5Le"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start_date = datetime(2011, 8, 1, 0, 0, 0)\n",
        "end_date = datetime(2023, 10, 4, 0, 0, 0)\n",
        "all_commits_to_analyze = get_commits_by_range(initial_date=start_date, final_date=end_date)"
      ],
      "metadata": {
        "id": "_MPbwIHia-La",
        "outputId": "b65355bb-cdeb-452f-c1fb-1b566c82a0ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wait...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress commit anlysis: 100%|██████████| 11732/11732 [05:43<00:00, 34.12it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.1 Analyzes commit messages\n",
        "\n",
        "Analisa todas as mensagens dos commits entre 2/03/2009 e 4/10/2023 do Kafka que contem as Keywords coletadas da literatura."
      ],
      "metadata": {
        "id": "xqjF0z8R05HF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start_date = datetime(2011, 8, 1, 0, 0, 0)\n",
        "end_date = datetime(2023, 10, 4, 0, 0, 0)\n",
        "\n",
        "my_satd_commit_analyzer = SATDCommitAnalyzer(path_to_repository)\n",
        "commits_with_satd, dict_commit_msg = my_satd_commit_analyzer.analyze_commits_for_satd(start_date, end_date, final_satd_keywords)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M9jUwjRxaG_5",
        "outputId": "99f1b634-77d3-4132-a5a6-ac1f2eb49299"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aguarde...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress SATD message commit analysis: 100%|██████████| 11732/11732 [00:05<00:00, 2067.24it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calcula a quantidade total de commits e a lista (hash) desses commits dentro da faixa analisada\n",
        "count_commit_analyzer = SATDCommitAnalyzer(path_to_repository)\n",
        "ve_qtd_total_commits, ve_lista_com_todos_os_commits = count_commit_analyzer.total_of_commits(start_date, end_date)"
      ],
      "metadata": {
        "id": "sn6PxA8eNcFl",
        "outputId": "48789700-6f5e-42da-8b18-0bd90ee13a27",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aguarde...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress messagem commit analysis: 100%|██████████| 11732/11732 [00:02<00:00, 4129.72it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Total de commits analisados: {ve_qtd_total_commits} na faixa: de {start_date} até {end_date}')"
      ],
      "metadata": {
        "id": "wxhOPgXKV9_G",
        "outputId": "e6b4cccd-bf6d-4171-95cb-21626a5d499e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total de commits analisados: 11732 na faixa: de 2011-08-01 00:00:00 até 2023-10-04 00:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.2 Analyzes commit diffs\n",
        "\n",
        "Analisa todos os diffs (added) dos commits entre 2/03/2009 e 4/10/2023 do Cassandra que contem as KL-SATD."
      ],
      "metadata": {
        "id": "nJRFVDfoaTy2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dict_commit_diffs = my_satd_commit_analyzer.analyze_commit_diffs_for_satd(start_date, end_date, final_kl_satd_keywords)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVYTPQKAaOZT",
        "outputId": "e95534d6-63f1-4798-ea04-386a38d0051c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aguarde...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress SATD diff commit analysis: 100%|██████████| 11732/11732 [03:09<00:00, 61.88it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.2.1 Cria um dataframe para representar o resultado da análise de SATD Keywords dos Diffs dos commits"
      ],
      "metadata": {
        "id": "G-C7FL4I2X6V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "l_commits, l_keywords, l_comments = [], [], []\n",
        "\n",
        "for k, v in dict_commit_diffs.items():\n",
        "  l_commits.append(k)\n",
        "  l_keywords.append(v[0])\n",
        "  l_comments.append(v[1])\n",
        "\n",
        "dict_commit_diffs_aux = {\n",
        "    'commits': l_commits,\n",
        "    'keywords': l_keywords,\n",
        "    'comments': l_comments\n",
        "}\n",
        "\n",
        "df_commit_diffs_aux = pd.DataFrame(dict_commit_diffs_aux)\n",
        "\n",
        "df_commit_diffs_aux.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jh-lkiridrVU",
        "outputId": "edca455f-e6ff-4e45-e7a6-ff598a416020"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 331 entries, 0 to 330\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   commits   331 non-null    object\n",
            " 1   keywords  331 non-null    object\n",
            " 2   comments  331 non-null    object\n",
            "dtypes: object(3)\n",
            "memory usage: 7.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_commit_diffs_aux"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "utQuSJz2flgw",
        "outputId": "038c2eee-db87-4058-bd0c-28793f6e3cc4"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      commits  \\\n",
              "0    642da2f28c9bc6e373603d6d9119ce33684090f5   \n",
              "1    af0ceff79b3f1902bf9d005136ace81549332902   \n",
              "2    a5fb217293e5124ef7f47e0d45c7400bd9286077   \n",
              "3    9fd2d4ffdba4b45df7807c1a70ff17496b51afa8   \n",
              "4    f8042d5b3932a0114c33cad6388eba9c41d77809   \n",
              "..                                        ...   \n",
              "326  16dc983ad67767ee8debd125a3f8b150a91c7acf   \n",
              "327  88b554fdbd47d4820059bc3633f5b2e3d2adb12d   \n",
              "328  0e47fa75371dda275de73610b414c16a639895f3   \n",
              "329  a7e865c0a756504cc7ae6f4eb0772cadd3333c53   \n",
              "330  7d89bdc3f0d1b7c477f728449841c45fcfbb5724   \n",
              "\n",
              "                                              keywords  \\\n",
              "0    [FIX, TODO, TODO, TODO, TODO, TODO, TODO, TODO...   \n",
              "1                                               [TODO]   \n",
              "2                                         [TODO, TODO]   \n",
              "3                             [TODO, TODO, TODO, TODO]   \n",
              "4                                               [TODO]   \n",
              "..                                                 ...   \n",
              "326                                       [TODO, TODO]   \n",
              "327                                              [FIX]   \n",
              "328                     [TODO, TODO, TODO, TODO, TODO]   \n",
              "329                                             [TODO]   \n",
              "330                                             [TODO]   \n",
              "\n",
              "                                              comments  \n",
              "0    [          /* however, if there is an option i...  \n",
              "1    [    // TODO: This cannot be a singleton since...  \n",
              "2    [        // TODO: fix up the max wait and min ...  \n",
              "3    [        // TODO: Remove this with leader elec...  \n",
              "4    [            //TODO: handle response in Produc...  \n",
              "..                                                 ...  \n",
              "326  [                    // TODO: enable regardles...  \n",
              "327           [     * prefix {@link #MESSAGE_PREFIX}.]  \n",
              "328  [        // TODO: review default selection,   ...  \n",
              "329  [        // TODO: integrate logic for refreshi...  \n",
              "330  [                // TODO: TimeoutException wil...  \n",
              "\n",
              "[331 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e7075155-e10b-4f32-a32f-6c36d12acc58\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>commits</th>\n",
              "      <th>keywords</th>\n",
              "      <th>comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>642da2f28c9bc6e373603d6d9119ce33684090f5</td>\n",
              "      <td>[FIX, TODO, TODO, TODO, TODO, TODO, TODO, TODO...</td>\n",
              "      <td>[          /* however, if there is an option i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>af0ceff79b3f1902bf9d005136ace81549332902</td>\n",
              "      <td>[TODO]</td>\n",
              "      <td>[    // TODO: This cannot be a singleton since...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a5fb217293e5124ef7f47e0d45c7400bd9286077</td>\n",
              "      <td>[TODO, TODO]</td>\n",
              "      <td>[        // TODO: fix up the max wait and min ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9fd2d4ffdba4b45df7807c1a70ff17496b51afa8</td>\n",
              "      <td>[TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        // TODO: Remove this with leader elec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>f8042d5b3932a0114c33cad6388eba9c41d77809</td>\n",
              "      <td>[TODO]</td>\n",
              "      <td>[            //TODO: handle response in Produc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>326</th>\n",
              "      <td>16dc983ad67767ee8debd125a3f8b150a91c7acf</td>\n",
              "      <td>[TODO, TODO]</td>\n",
              "      <td>[                    // TODO: enable regardles...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>327</th>\n",
              "      <td>88b554fdbd47d4820059bc3633f5b2e3d2adb12d</td>\n",
              "      <td>[FIX]</td>\n",
              "      <td>[     * prefix {@link #MESSAGE_PREFIX}.]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>328</th>\n",
              "      <td>0e47fa75371dda275de73610b414c16a639895f3</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        // TODO: review default selection,   ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>329</th>\n",
              "      <td>a7e865c0a756504cc7ae6f4eb0772cadd3333c53</td>\n",
              "      <td>[TODO]</td>\n",
              "      <td>[        // TODO: integrate logic for refreshi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>330</th>\n",
              "      <td>7d89bdc3f0d1b7c477f728449841c45fcfbb5724</td>\n",
              "      <td>[TODO]</td>\n",
              "      <td>[                // TODO: TimeoutException wil...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>331 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7075155-e10b-4f32-a32f-6c36d12acc58')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e7075155-e10b-4f32-a32f-6c36d12acc58 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e7075155-e10b-4f32-a32f-6c36d12acc58');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-eba8cbbd-cb3f-4f5e-9b20-95cdb9265702\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eba8cbbd-cb3f-4f5e-9b20-95cdb9265702')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eba8cbbd-cb3f-4f5e-9b20-95cdb9265702 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list_commit_with_satd_keywords = df_commit_diffs_aux.commits.to_list()\n",
        "print('Commits com SATD Keywords: ')\n",
        "print(f'{len(list_commit_with_satd_keywords)}, {list_commit_with_satd_keywords}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yU9Vg4mefpvD",
        "outputId": "d409cd2f-d2eb-4d3a-cecd-cccde1ac386f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Commits com SATD Keywords: \n",
            "331, ['642da2f28c9bc6e373603d6d9119ce33684090f5', 'af0ceff79b3f1902bf9d005136ace81549332902', 'a5fb217293e5124ef7f47e0d45c7400bd9286077', '9fd2d4ffdba4b45df7807c1a70ff17496b51afa8', 'f8042d5b3932a0114c33cad6388eba9c41d77809', '4dd7f95569d3af60c73484e7969080ac0d755085', '854fb29c26ed55926956e858b0465b85f80ef738', 'c0e190cdefabc7c59b11bbe0de843baec78efe3f', 'd73355017a66fbcdf0a6ad1bafbf3d60fa004b92', '822963bb9dab9c948ecf850ab25b311e9d9fa7ae', '045f4fdca4f8305a537d0e382f88aed3da0965a7', 'f99177766aff632831598a1e2e2c929a015b7c68', '83c82a3ecc734138ed561a69f18ca3ac02a41ddb', 'eff68ce1d0ef59e27a49edea4759ad0cd4096b7a', 'e7efee02c10e2def27d6b301e2f926202dabb9ce', '2a9d42c243397142f80d3f0f96c8d7a6b74d1b02', 'c21a7979c18c93336bd1dda5f82627c9ae537b62', '0223fd827003b94babdf6cb6624d0f97e936d00a', '663d25443359763b2d0fb8adcbdd96cc82dcbda7', '22e4341ba9f7744314ae0363529333a276bd325d', '2bc65dab67933e9f9cdade59bd0e45ff99afd338', '3889a3b34fd358ce512efe8c44c44befc19e3273', 'f64fd3dcbaace1dba7bbd72398bb3e7d28b41d61', '01a8b8d12377eae66a1924d2e26ed4e94d3f52bc', '6d0042b4f4125b3731d3b85a0e741f1dde1413fe', 'a1698e2b70341cae20c53fb6b2e890df34b29a3e', '23d7620442df299f43d147a2ad924b2759f7efbd', '3244bcafe4233611fc0366f9df53ec98b44ca571', '03eb903ce223ab55c5acbcf4243ce805aaaf4fad', 'e7edb5e1e933f5535378d546bcf4d8b178d2e69c', '28ee7855360b8e6ea690fc802dac4eaa60ad81e2', '9753d15ef1da0ecab9bbee51d8cdc08c2159c50b', '1cb217579bbb1f9d4df24a3d32bf88644697b7fe', 'bd49e4f3e77c86a9cd93262e628143fff762d4ee', 'ec547737de730cddb29200eba62c0165f901447b', 'f63e3f730673f60ca179030cf0d64380689c2a40', '269d16d3c915d09f650ae32aa81542bd8522ca68', '167acb832d7f104eb2c344dcfd7b914c763d881d', 'd6303ec79e6871c901951e7dcde93d4996b52c49', '84a5803a7e5235e3fa0af7de8e40bdf77ff9affe', '3d830c9ef1a186cd36e4f5c7bdc2dfef2d51edc7', '5e2a9a560d847bd0cf364d86bd6784f70d99c71a', 'a670537aa33732b15b56644d8ccc1681e16395f5', 'c24740c7b0f6a6e7c66659da786a346650b76766', '0dc243b92a6ae1683795caf8222edc1b2bb49565', 'c6f08b6094924ff3560a8e16090c85ed1a415b03', '37356bfee09bf0dffc93d85270c342e03c36ca44', '89831204c092f3a417bf41945925a2e9a0ec828e', '5174df53778cb5cb2d6d86e4cec9f3185a2c85db', '0699ff2ce60abb466cab5315977a224f1a70a4da', '1c6d5bbac672cbf49591aed0889510b10e3285fa', '71602de0bbf7727f498a812033027f6cbfe34eb8', 'b8904e9614b4c1f11b8487e7a4b88b1e37e1f20b', '0b92cec1e07a1f2d9aa70f3ecd7d0fb12290d2e2', '01f20e029fbd068a8493492a2c4a1364a5668579', 'c41c7b40b63ecd668c727a897f29e276a1c5adf7', '49026f11781181c38e9d5edb634be9d27245c961', 'd22987f01d50549d855ae092b69f520d75bfeb7b', 'b6d326b0893e60b350608260fd1bd2542337cb5a', '3f8480ccfb011eb43da774737597c597f703e11b', '99c0686be2141a0fffe1c55e279370a87ef8c1ea', 'f6acfb08917946f15cb8de2fee786019124af212', '9e2c683f550b7ae58008d0bcb62238b7a2d89a65', '689d170ac32ed15af9098faa03a97609af14bf05', '9dbeb71ab258955e04b46991c1baf880b07633f4', '48b4d6938dfb9dc72c941e979ff0d0c4d921f743', '263c10ab7c8e8fde9d3566bf59dccaa454ee2605', '5e769ccd5bf81f82c90069d4b0e79182a1cfe3c0', '36d4469326fe20c3f0657315321e6ad515530a3e', '403158b54b18cabf93eb15d4c4dd8ab66604bf9f', '86eb74d9236c586af5889fe79f4b9e066c9c2af3', 'd9b1dc70817de9366513b0cbed0b6e0702d473fa', '2e61773590c0ba86cb8813e6ba17bf6ee33f4461', '1b5687b9e4cd10e0f91fb921d3569fdd44be163d', 'c001b2040cb7bf3a24aa77ec8de1312d4f780620', '7c33475274cb6e65a8e8d907e7fef6e56bc8c8e6', 'f4b87deefecf4902992a84d4a3fe3b99a94ff72b', 'e0098b4567541d1c0cefc6f57ce38f67a9133b5e', '45e7f71309f9a3e30d25a6ddd3171c67e3e79286', 'dbdec927b9badcdbe4a4b4b6ccebf5044ca32747', '39c3512eceedebcb6e50f8c6c4ef66601ff7dbc4', 'd05fa0a03bc9bcfcff8d73cbf1b22832ebdb75a2', '5bc90d4542ca9d6cea3eac1e0a7e1bfa1fed15e7', '40d731b8712950122915795acca43886851a73b6', 'a62eb5993f5517a64dd1020b0a9bbd1012f7ee67', '959cf09e8653f4b8255f49c6f4c258ed1a5ec38e', '5ae97196ae149e58f6cfa3c5b6d968cbd7cb6787', '57da044a991ebf8913d44dfcfa6a27729f54a4d5', '79eacf6c95506d5d6819add5a1256681b13170b1', '77683c3cb0eab8c85eb13d0e1397cf9ee32586b6', 'f141e647a4a3daf618b48073058320ecaa3671d0', 'ff7b0f5b467bdf553584fb253b00f460dfbe8943', '3358e1682f034a82afd670be95505b2f620c78c6', '878b78acb68d0fa62eb3c8f21d572664ca2bcdd0', '845c6eae1f6c6bcf117f5baa53bb19b4611c0528', '00a58f8e1e0c82c2948a8fdfacf812ec4865b339', '9c4c5ae1cd15aa0afe3156e572362fbb40130573', '30e78fa00650b258f3ab5ef6c9bdf5ca137289c0', 'dea0719e99211684775780f5da8b93835d7a5dac', '3a58407e2e0aee0bb2c5e343fbe98c7f28fc6f3e', '667ff7ef737612773c50908d2b3cc829bb5132c7', '89c67727c2793bf56b0b005a7d758beebedb5aed', 'c9485b78a6e43747daf1314ae9532839fb7bc810', 'a02c8aaecfbd13838c2a062bac1455da352028fe', 'fb42558e2500835722a4e5028896ddae4f407d6f', '8c551675adb11947e9f27b20a9195c9c4a20b432', '751fe9309011b99f60c1cb03c23a47d0444dce05', 'a5da2a4f4c3b9d1917cbafc0e79d8e8318f7cbff', 'fc47b9fa6b89a612cd0f79dfc689ee3813fd405a', 'bb629f2243c4462db2a863793c190d734f11f3c6', 'be36b322749003581474e2c84a3ec9ba2aaec53c', 'ae237be1bbfd7d1b84a5095491a6131cd3cc9346', 'f903215536af06b7b79739882d9286abc2e50000', 'a2bac70a6634e9a78734d23158fb7e45f290ea26', '6cf2cb6f294d0d4766f57e9da660fb4efac7d258', '143a33bc506c703ea02fe5daa61f90b7094da13c', '3b4c347949c02b1e2b1dd473deda0f8d2304d027', '1949a76bc4189534b853e21c476bb11172fa3fc9', '6f7ed15dad8d914ae65a595eef327c0510f11469', '4b71c0bdc1acf244e3c96fa809a1a0e48471d586', 'e90db3e3af0718a80d729809186d45076e71a241', '3400d0c3cc07d5d3136bf9a19142b36cca93f92d', 'ca0c071c108c9fd31a759e1cd1c4f89bdc5ac47e', '4c42654b1eecebae272dfe5ce018b85ad4db7709', '7ebc5da606fb806bde8b7176fed0e60243e0f7f3', '4db048d61206bc6efbd143d6293216b7cb4b86c5', '57278aa82da5dc9d040eb3dcf4a182e0731a621b', '5bd06f1d542e6b588a1d402d059bc24690017d32', 'bdf4cba047334943aa8357585c4fb379b27e9ffd', 'd0e7c6b9304b23ced046934c799df0cba39c28e5', 'f69d94158ce72f4ef2df5b98f2395c2b2e61251e', 'e71dce89c0da50f3eccc47d0fc050c92d5a99b88', '0eedd6d806ffc2f8e1e2a704d831bc5ac865ce3e', '4951849163b1defea91129472b5354531407deb9', '794e6dbd14f040d21d3402c5eda22cfa8f5c4b3d', '3e6669000f082808999a7216b00c4b0f5a94e1da', '1cea4d8f5a51cc5795ddd3af2ea015b9e14d937d', 'c64cfd2e2bffd0a8fcfa515d3bbbe76416a89ee7', '45f2261763eac5caaebf860daab32ef5337c9293', 'e28752357705568219315375c666f8e500db9c12', 'b04bed022aec0f1f478a03383ab5184f048133b6', '1110f66fa7278212160c6e8392b6e6ab09560452', '5d798511b12c5ef7555e4234fdd99a360176e435', '1844bf2b2f4cdf5a8209d7ceccb6701fc7dcf768', 'da2205578be3228ce40eb5e59f6bbcb0c8da0aba', '228a4fdb6dc15d1b8615615e00825e7ce53a41fa', '125d69caee993710bc07523fdd3deb0e06b10636', '1f659913a631d9e77e7cd9b9c9775e3249ef803a', 'eaabb6cd0173c4f6854eb5da39194a7e3fc0162c', 'f9865d52e81bbdddb7889d6c3cc7be537e610826', 'b71ee043f8959ee0d4699071ba8fc1e2c5675842', 'e4208b1d5fa1c28ac7e64e2cb039404a14084dc0', '4fac83ba1f80353e9544b15b95b8da9dc557041d', 'e3c32391f95d82a217295c7e4c1519981124bc3f', '5df1eee7d689e18ac2f7b74410e7a30159d3afdc', '043951753b6fb6c8bae6d25d7a6a97e74b614cac', '82c6d429e70f1c782103953072dc6dbec650dd6e', '3e21e17a7d3613bfab8c34555c0f598a07cf0675', 'b814a16b968d144802d08523b5c359d6706f5632', 'b1aa1912f08765d9914e3d036deee6b71ea009dd', '394aa7426117d0d04666c1c2a63d5f98229b7894', 'b599b395f3d421b3fdb1d25b294778478c2467cf', '1b170df31c7304f6d4c938b5e0c2a09ae1e9189d', '515ce21c74743171df6cb069b4754f68fe7f2233', 'af983267be7a2d0f81527f5a348af377f30caee4', '2d8049b713f2b9982bd43e7340a0e0f302f30d6b', '1e207b2ef83db4296fb944ade4c301794f9e8460', '70a506b9839605304a0a67b70e711a1139b47cbe', '837f31dd1850b179918f83338b4b4487486b2c58', '6f9f3655731ea1d46bd1f0ed0957579d831e2692', 'b3aa655a70d92fedfebd32cf469a87b45766fc59', '0b3989fd72acc6e989c495c63cb8ca6aa5850811', 'd98ec33364d4ddb6ee53e0cd6001857207ba2089', 'a5924025126a6ab77fd9fb74cf66a180931ffecf', '642a97783d29bb1d43ee20e8564db7b9432dade2', '1354371d4f4dd208458c776e1f27715ec5f77f47', '57320981bb98086a0b9f836a29df248b1c0378c3', '3c14feb442219f58e0008a6242a24f1a492ca337', '0d778987ee3b920b5c876696385c0792671adef8', '283a19481d1ce4a77f5f465e7b96288db22a8ff1', '057c5307e0f055b37794453d16f6f43b8c56528c', 'f712ce69fc2cb6a164b1ea8e491c9d68e6661933', 'b793eaade4fcb0705ab80c2a806331fab8c29f9f', '93466602d80c0c8dba7544964f48feb49589b0b5', 'c0353d8ddce88bac6fc04f85dd40cb95b8ca5cf9', '9d544212e69269f155bb3a51f94a9b13cf1fa565', 'e4cad353124478fd4034f18d2292ba62fb702924', 'd63d7022526565a96323a45fad4ead6c3db573ad', 'b7d7f7590de405d8d527c32ca9f067e073e9d83b', '58d6043d4c88892f6718e31799503c3482000ea2', '17c80166461c0005b7603414db4d0a3541df0f82', '8649717d6dde081c75fc441a56f63ee1556dc758', 'ce584a01fff4e73afa96d38c9a7508fcd67e3e46', 'e6563aab722b35c4984b77e9eee42a1904cd1ea6', 'e867a58425876767b952e06892c72b5e13066acc', '24547b810ce9aae4389dad7f41cc47caba73046c', '83c7c0158fc62c294267f0ae60e9f52945e6a678', '1c831c22e13fcc59e7e1a9e6c28fd38048eb3f62', '791d0d61bf5a2b7a4fda72d4c50075a1933a6af3', 'f98013cc501ab30c43ca707a1fbf46c4e9dc1215', '5a65da5fe96bc44b5323f001263e487144e50f06', '7746301c2643e0037bff3bcb84c41d512937e4c9', '4090f9a2b0a95e4da127e4786007542276d97520', '1681c78f60a3a75e69e2222be0649cd61f02f042', 'd8756e81c57040130ec163e63798663d62298589', '3b6573c150527efb3e90b2b3d1a059ac5c2db80b', '97d107a270d3e9b795b42d209f838f2f81009ce8', '71113b4aac43a3a6f13625e25171ef8f2233fe7d', '89cd2f2a0b21368297323437fd15ba6341e4707b', '1ad5f346cb88072c419142c86c3d897aad2de996', 'ee832d7d78009e82a968eed28644bf97a34170e2', '353aa6206d43b4923d21510797709bbbb210f6b6', '58f7a973149f66b980f99c3f2bd688d70b06b2eb', '27824baa210b9744074ac1fcdabbf1c8ff1b42ca', '07f66765396bffa7e45edeb141726a9a55ead7e0', '84051e0e886de46b8320e3ee222d380b00459b26', '0f68dc7a640b26a8edea154ea4ea2b6d93b5104b', '7d8e62b3b6d5a4f414211eb9cd5caa0e77d72cfb', '8e083e1b8879e732522bb9a560c82ff84b176cc9', '7f4fc76e968a6b2cf4a73364c93bfdea03f81af3', '2239004907b29e00811fee9ded5a790172701a03', '3b2ae7b95a27176a22e5356160a9df468c483778', '715df0d27122e0799c68daa78ab5f9ce0ab6b594', '1112fd47238abd0254ceb11d699478d4451278f7', 'b351493543b7e26aa345df3b568d0dc08a8c8d91', '7915d5e5f826a71c11e1c9183c84702410f7209a', '9da32b6bd014f1bdeeee5da8fcd00995a5916323', 'c2273adc25b2bab0a3ac95bf7844fedf2860b40b', 'b7c8490cf47b0c18253d6a776b2b35c76c71c65d', 'aef6cd6e9995b42db2cefa7d715321d0edee5628', 'cb3dc6785bc8263d8e1fc695e499e1eeb9df52a4', '192db666c913c3fb2bfe3568ac51aa4ddb010a25', 'eb9fe411bbbfca1baf5e0900ee9cfb9147e5099e', 'b6891f6729e9997b18ffca2515cb0265520a1874', '393139064f79ee664b6d8507ea1f974dad84c82e', '0158e1d7196748aa346da90f688048a32f75c2d1', 'd2cb2dc45d536ae124e3da25d6d5a4e932a23a27', '31a647f5adf0d1ff8f190cccc0da900cfbaf2756', '25555b89f5155e4c9cbd474674cc6f24b0121b29', 'a30f92bf59cf31fc1dd36eadc2b9e554e0aef900', '690f72dd69d31589655c84d3cc1a6eec006bcab5', '5eac5a822fe4eb8691d2411fb25fcba1d4203b55', 'fb2eef94a4ae91501d869e2075c511b433877099', '40f001cc537d6ff2efa71e609c2f84c6b934994d', 'e28924062f6cf5702080ca8b8473019bcb09ecae', '327809024fab07c44cd6d87985db5b9d14fd43bb', '16b2ce7da72e60d88b6ca8a3eff853a28a1458f9', '4b2736570c9d5f4b6e501ab8cd5efe696a5142b7', 'b2d463aa12feee5b3207d1afea5e6b0b6fb9656f', '48379bd6e5075fc73449e235f1b527114f5a430c', '670630ae5b81d8d1cd32defe8824640dd865a538', 'b4e45cd0d245eb66a9f3d2d69523a507bd820dff', '7bd55f51567d08d35922cf5c23a4b424e58fff9a', '2b8aff58b575c199ee8372e5689420c9d77357a5', '7fbc6b73aa5d194948b30a00074cd0f79365694d', '4710a491463a91ec12c670ea50c139fc14134e80', 'a103c95a31ee276e936669438828bdb6e73d5f14', '6854eb8332d6ef1f1c6216d2f67d6e146b1ef60f', 'a0c7e6d8b4945f01f0ea956a66f05d57b92937f0', '1b0294dfc4943421cef50365fc698508d2806965', '71f1cb7bfb2dc801c9ea47e0794d915e7f279635', '908a6d2ad7807642b917231769c8d3b6a0c1ad16', '14c24490505090b0bedeabd891542915692501be', 'a957748d2e0de872e881ec17f6cc27ada7ab7cd9', '68a19539cf1fcd86787960d0010b672d0d611b91', '265d3199ec741928c1731397302f6c320cf6af40', '14c6030c6ad70997863070f5b93c817ea8829425', '7215c90c5e9086126cbecbb5d8b3bfee22f6d1fa', 'd35283f011a797902fc9c4d896a1a6f039eb7d06', 'd8cf47bf28af0b5eb5be8382883e6a331b788e01', 'cd4a1cb4101abcc7cdd1d2d0d73662114108f3e5', '6eb57f6df1706c6ec228123cea1c120bd53b62d6', '029a14b530dcaab980faee4fa18c925b95db3122', '539f006e65d9060cd46a4052d1b70f2312d8ca34', 'b68463c250ec99d86ffacdbd45e58059d0ca51e1', '0924fd3f9f75c446310ed1e97b44bbc3f33c6c31', 'fa0324485b1e76388bb15ea98170cfb9202aab1f', 'cc2aa96ae4063bdc50d692eaf5d11becbb94154b', '7268284699f84c7f61f5656167c36877b72d27f2', '46efb72600e26ac4211a2defac1ff44f8734b404', '9e8ef8bb317599c184ce8201d494edf109d9c528', '4d53dd99720ca4605a2975d2f14c58b80a965550', 'd00b7875e0ad7e005b9b54d39f47269c71e95fbe', 'ababc4261bfa03ee9d29ae7254ddd0ba988f826d', 'c9b6e19b3b37499de17da19e82b1b98e3b9f6b5c', '06f47c3b517f5c96c1bd981369843f3483947197', 'ac64693434a66ad76bfc401a1a89ab2dbeaaeaa8', 'dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd', '8380d2edf48d3e97ebf0c2312a8127960232b947', 'ba89eaf0d7f3e4dfc3742db418faf9147ebb9363', 'c3690a3b4ada3bd756df39cdf2974faf3898505a', 'dfb592966549a2a9f2acda72ca21f89753740881', '0a045d4ef7d67dbe35b8fd2e1c51df87af19a0ab', '7a93d5c8331b405ac2eb517a4a84396c515517bd', 'fca5bfe13cb528b800ec48819c072b3130894009', 'd40561e90ab5b1f5c79d174393645c22b5797eff', '67c72596afe58363eceeb32084c5c04637a33831', '8b045dcbf6b89e1a9594ff95642d4882765e4b0d', '8184ada6a5c98cd637e8d323c56dbe6b20d582bd', 'db490707606855c265bc938e1b236070e0e2eba5', '896573f9bc2c6a695cb265df027c4f21214bc933', 'ee8e7578789e02e369ce933341501a383b62d5dd', '65bb819313bbfe21f021f925b49a26e7ce0c954d', '2fad1652942226454a44038f2350642817f9f74b', '8d32a0f2463eaa4c0b669b8a957a7abc066c066f', '62431dca700fb2c7c3afe1a7c9eb07fe336f9b04', 'e3817cac894140e0b26c09050b10ce653f7c3d97', '6fbe4d85a22872b97ec45614cc219d3a1da42f20', '897ced12eebe7cea7a5ed9227f463107d858fbc2', '5c0e4aa67642ac0df1bbb79be84ef579b04194c2', 'b2ee6df1c4cfee921deb4e8b9ea48ee0939d85dc', 'f02f5f8c8a8b1f196a1904fe2bd78f228980e1d6', '750a3893081fbe1e01f8c6eadf84726ace1eb47a', 'baf127a6633161cb52747467880b006d2f54d3bd', 'c1b5c75d9271638776392822a094e9e7ef37f490', '0822ce0ed1a106a510930bc9ac53a266f54684d7', '49d9c6775dedd1abcf45683d6c9e3d9c6c05db9c', '6fe74f78dc725466319bd0465764913532949ba3', '474053d2973b8790e50ccfe1bb0699694b0de1c7', '726d277c0aac6bfc57a16ef240740b7875ce75ae', '0e56cc88416faf31485343ebb95948cb5d8fbc06', 'cbd5d52ec7b89a5bbbbc245f5373a98d8379ab5a', '1656591d0b339c385d0ba1f938fc94b52e29965d', 'ed44bcd71b3b9926c474033882eaa6c1cf35cfa4', '4f88fb28f38f3e461377bf688520b28a3f209b5d', '945d21953e93409290a68dced0d366380866fb26', '16dc983ad67767ee8debd125a3f8b150a91c7acf', '88b554fdbd47d4820059bc3633f5b2e3d2adb12d', '0e47fa75371dda275de73610b414c16a639895f3', 'a7e865c0a756504cc7ae6f4eb0772cadd3333c53', '7d89bdc3f0d1b7c477f728449841c45fcfbb5724']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Converte a lista de commits para um conjunto')\n",
        "set_list_commit_with_satd_keywords = set(list_commit_with_satd_keywords)\n",
        "len(set_list_commit_with_satd_keywords)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eL9eLv3Zfs-q",
        "outputId": "eced904b-ec7a-4043-d674-5a0cea02b334"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Converte a lista de commits para um conjunto\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "331"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.3 Lista de Arquivos Críticos\n",
        "\n",
        "Projeto Kafka (https://github.com/apache/kafka)\n",
        "\n",
        "Lista de arquivos críticos do Kafka impactados por ATD\n",
        "\n",
        "Obs: extraídos via ATDCodeAnalyzer (https://github.com/mining-software-repositories/cassandra)"
      ],
      "metadata": {
        "id": "xNuEGCkLfxlF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Funções de apoio"
      ],
      "metadata": {
        "id": "6OavZlOzf3Cb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Conta a quantidade de vezes que um arquivo critico apareceu na faixa de commits analisados\n",
        "def get_dict_commits_count_by_critical_files(lista_arquivos_criticos, my_commits_to_analyse):\n",
        "  dict_count_critical_files = {}\n",
        "  for file in lista_arquivos_criticos:\n",
        "    dict_count_critical_files[file] = 0\n",
        "\n",
        "  for k, v in my_commits_to_analyse.items():\n",
        "    message, critical_files, dict_of_diffs = v[0], v[1], v[2]\n",
        "    for file in lista_arquivos_criticos:\n",
        "      if file in critical_files:\n",
        "        dict_count_critical_files[file] = dict_count_critical_files[file] + 1\n",
        "\n",
        "  return dict_count_critical_files\n",
        "\n",
        "def get_df_critical_files_in_commits(lista_arquivos_criticos, my_commits_to_analyse):\n",
        "  \"\"\"Prints the number of times each critical file appears in the commits.\n",
        "  Args:\n",
        "    lista_arquivos_criticos: A list of critical file names.\n",
        "    my_commits_to_analyse: A list of commits to analyze.\n",
        "  Returns:\n",
        "    A Pandas DataFrame containing the number of times each critical file appears in the commits.\n",
        "  \"\"\"\n",
        "\n",
        "  # Get the dictionary of commit counts for each critical file.\n",
        "  dict_count_critical_files = get_dict_commits_count_by_critical_files(lista_arquivos_criticos, my_commits_to_analyse)\n",
        "\n",
        "  # Create a list of file names and commit counts.\n",
        "  list_name_files, list_qtd_commits = [], []\n",
        "  for k, v in dict_count_critical_files.items():\n",
        "    list_name_files.append(k)\n",
        "    list_qtd_commits.append(v)\n",
        "\n",
        "  # Create a dictionary of critical files and commit counts.\n",
        "  dict_critical_files_in_commits = {\n",
        "      'file': list_name_files,\n",
        "      'appears_in_commits': list_qtd_commits\n",
        "  }\n",
        "\n",
        "  # Create a Pandas DataFrame from the dictionary.\n",
        "  df_critical_files_in_commits = pd.DataFrame(dict_critical_files_in_commits)\n",
        "\n",
        "  return df_critical_files_in_commits\n",
        "\n",
        "def find_all_issues_cassandra(input_string: str) -> list[str]:\n",
        "    \"\"\"Finds all Cassandra issue ID patterns in the input string.\n",
        "    Args:\n",
        "        input_string: The input string.\n",
        "    Returns:\n",
        "        A list of Cassandra issue IDs, if found; otherwise, an empty list.\n",
        "    \"\"\"\n",
        "    # Try to find all Cassandra issue ID patterns in the input string\n",
        "    matches = re.findall(r'(KAFKA-\\d+)', input_string)\n",
        "    set_matches = set(matches)\n",
        "    list_unique_matches = list(set_matches)\n",
        "\n",
        "    # Return the list of matched Cassandra issue IDs\n",
        "    return list_unique_matches\n",
        "\n",
        "def get_dict_commits_with_issues(my_commits_to_analyse):\n",
        "  dict_commits_with_issues = {}\n",
        "  for k, v in my_commits_to_analyse.items():\n",
        "    message, critcial_files, dict_of_diffs = v[0], v[1], v[2]\n",
        "    if len(find_all_issues_cassandra(message)) > 0:\n",
        "      dict_commits_with_issues[k] = v\n",
        "  return dict_commits_with_issues\n",
        "\n",
        "def get_dict_only_commits_with_issues(my_commits_to_analyse):\n",
        "  dict_commits_with_issues = {}\n",
        "  for k, v in my_commits_to_analyse.items():\n",
        "    message = v[0]\n",
        "    if len(find_all_issues_cassandra(message)) > 0:\n",
        "      dict_commits_with_issues[k] = v\n",
        "  return dict_commits_with_issues\n",
        "\n",
        "def get_df_critical_files_in_commits_with_issues(lista_arquivos_criticos, dict_commits_with_issues):\n",
        "  \"\"\"Prints the number of times each critical file appears in the commits with issues.\n",
        "  Args:\n",
        "    lista_arquivos_criticos: A list of critical file names.\n",
        "    dict_commits_with_issues: A dictionary of commits with issues, where the keys are the commit hashes and the values are the lists of files changed in the commit.\n",
        "  Returns:\n",
        "    A Pandas DataFrame containing the number of times each critical file appears in the commits with issues.\n",
        "  \"\"\"\n",
        "\n",
        "  # Get the dictionary of commit counts for each critical file.\n",
        "  dict_count_critical_files_with_issues = get_dict_commits_count_by_critical_files(lista_arquivos_criticos, dict_commits_with_issues)\n",
        "\n",
        "  # Create a list of file names and commit counts.\n",
        "  list_name_files, list_qtd_commits = [], []\n",
        "  for k, v in dict_count_critical_files_with_issues.items():\n",
        "    list_name_files.append(k)\n",
        "    list_qtd_commits.append(v)\n",
        "\n",
        "  # Create a dictionary of critical files and commit counts.\n",
        "  dict_critical_files_in_commits_with_issues = {\n",
        "      'file': list_name_files,\n",
        "      'appears_in_commits_with_issues': list_qtd_commits\n",
        "  }\n",
        "\n",
        "  # Create a Pandas DataFrame from the dictionary.\n",
        "  df_critical_files_in_commits_with_issues = pd.DataFrame(dict_critical_files_in_commits_with_issues)\n",
        "\n",
        "\n",
        "  return df_critical_files_in_commits_with_issues\n"
      ],
      "metadata": {
        "id": "jb6pBCwrf2fv"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Relação de Arquivos Críticos\n",
        "\n",
        "Lista de arquivos críticos extraídos do ATDCodeAnalyzer"
      ],
      "metadata": {
        "id": "0Es76BXif_HV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista_arquivos_criticos = ['StreamThread.java', 'KafkaConsumer.java', 'StreamTask.java', 'Fetcher.java', 'KafkaStreams.java','KStreamImpl.java', 'KafkaProducer.java','StreamsConfig.java', 'ConsumerCoordinator.java']\n",
        "lista_arquivos_criticos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-D9I8UpgCH1",
        "outputId": "40496a27-3a92-4999-851c-50d24ad500d6"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['StreamThread.java',\n",
              " 'KafkaConsumer.java',\n",
              " 'StreamTask.java',\n",
              " 'Fetcher.java',\n",
              " 'KafkaStreams.java',\n",
              " 'KStreamImpl.java',\n",
              " 'KafkaProducer.java',\n",
              " 'StreamsConfig.java',\n",
              " 'ConsumerCoordinator.java']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Análise dos commits que contem Arquivos Críticos\n",
        "\n",
        "- Filtra apenas os commits que contem pelo menos um arquivo crítico"
      ],
      "metadata": {
        "id": "LzYrevaEgGgZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Analisa a faixa de commits entre: initial_date={str(start_date)}, final_date={str(end_date)}')\n",
        "print('Registra apenas os commits que contem pelo menos um arquivo crítico')\n",
        "commits_to_analyze = get_commits_by_range_and_critical_files(initial_date=start_date, final_date=end_date, critical_files=lista_arquivos_criticos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eSFV5zDAgHra",
        "outputId": "b0695022-2bbf-49b3-d2d5-71bffa1f8053"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analisa a faixa de commits entre: initial_date=2011-08-01 00:00:00, final_date=2023-10-04 00:00:00\n",
            "Registra apenas os commits que contem pelo menos um arquivo crítico\n",
            "Wait...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress critical files commit anlysis: 100%|██████████| 11732/11732 [03:39<00:00, 53.36it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_qtd_commits_to_analyze= commits_to_analyze[1]\n",
        "my_commits_to_analyze = commits_to_analyze[0]\n",
        "qtd_commits_with_critical_files = len(my_commits_to_analyze.keys())\n",
        "qtd_commits_no_critical_files = my_qtd_commits_to_analyze - qtd_commits_with_critical_files\n",
        "\n",
        "print(f'Total de commits analisados: {my_qtd_commits_to_analyze}')\n",
        "print(f'Commits que possuem pelo menos um arquivo crítico: {qtd_commits_with_critical_files}')\n",
        "print(f'Commits sem arquivos críticos: {qtd_commits_no_critical_files}')\n",
        "print('\\n')\n",
        "\n",
        "plt.pie([qtd_commits_no_critical_files, qtd_commits_with_critical_files], labels=[f\"No Critical Files ({qtd_commits_no_critical_files})\", f\"With Critical Files ({qtd_commits_with_critical_files})\"], autopct=\"%1.1f%%\")\n",
        "plt.title(f\"Total of Commits Analyzed: {my_qtd_commits_to_analyze}\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "KPUhG5whg0o7",
        "outputId": "0f0fd213-c7f6-4787-e081-b647dbe8ee14"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total de commits analisados: 11732\n",
            "Commits que possuem pelo menos um arquivo crítico: 1452\n",
            "Commits sem arquivos críticos: 10280\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2. Quantidade de commits com arquivos críticos\n",
        "ve_lista_commits_com_arquivos_criticos, ve_qtd_commits_com_arquivos_criticos = commits_to_analyze[0].keys(), commits_to_analyze[1]\n",
        "\n",
        "print(f'Quantidade de commits com arquivos críticos: {ve_qtd_commits_com_arquivos_criticos}')"
      ],
      "metadata": {
        "id": "3Ox_XEtdYfUk",
        "outputId": "d8c3fa0d-9973-4340-e61a-707574bc4098",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quantidade de commits com arquivos críticos: 11732\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MESSAGE_COMMIT = 0\n",
        "CRITICAL_FILES_COMMIT = 1\n",
        "DATE_COMMIT = 2\n",
        "LINES_COMMIT = 3\n",
        "FILES_COMMIT = 4\n",
        "DICT_DIFF_COMMIT = 5\n",
        "MODIFIED_FILES_COMMIT = 6\n",
        "DICT_MODIFIED_FILES_COMMIT = 7\n",
        "\n",
        "l_commits_aux, l_msg_aux, l_critical_files_aux, l_date_aux, l_lines_aux, l_files_commit_aux, l_diff_commit_aux, l_modified_files_aux, l_dict_modified_files = [], [], [], [], [], [], [], [], []\n",
        "\n",
        "dict_commits_to_analyse = commits_to_analyze[0]\n",
        "for k, v in dict_commits_to_analyse.items():\n",
        "  l_commits_aux.append(k)\n",
        "  l_msg_aux.append(v[MESSAGE_COMMIT])\n",
        "  l_critical_files_aux.append(v[CRITICAL_FILES_COMMIT])\n",
        "  l_date_aux.append(v[DATE_COMMIT])\n",
        "  l_lines_aux.append(v[LINES_COMMIT])\n",
        "  l_files_commit_aux.append(v[FILES_COMMIT])\n",
        "  l_diff_commit_aux.append(v[DICT_DIFF_COMMIT])\n",
        "  l_modified_files_aux.append(v[MODIFIED_FILES_COMMIT])\n",
        "  l_dict_modified_files.append(v[DICT_MODIFIED_FILES_COMMIT])\n",
        "\n",
        "dict_commits_to_analyze_aux = {\n",
        "\t'commits':l_commits_aux,\n",
        "\t'msg': l_msg_aux,\n",
        "\t'critical_files': l_critical_files_aux,\n",
        "  'data': l_date_aux,\n",
        "\t'lines': l_lines_aux,\n",
        "  'files': l_files_commit_aux,\n",
        "\t'diff_commit': l_diff_commit_aux,\n",
        "\t'modified_files': l_modified_files_aux,\n",
        "\t'dict_modified_files': l_dict_modified_files\n",
        "}\n",
        "\n",
        "df_commits_to_analyze_aux = pd.DataFrame(dict_commits_to_analyze_aux)\n",
        "df_commits_to_analyze_aux"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "id": "i4ErSIwUhDJa",
        "outputId": "73147273-cb94-4436-8bd1-4eacd0ec590f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       commits  \\\n",
              "0     269d16d3c915d09f650ae32aa81542bd8522ca68   \n",
              "1     253f86e31062fb86401abdc13835c251eef47417   \n",
              "2     fa6339c19cd06880d32ec9a5ee6b66e7f1488dcf   \n",
              "3     36eae8f63c81f8d38c2351054c15bf7d5eb612d9   \n",
              "4     7e154a36f74ad0ea7e0f6d48b71a5a73d99330e7   \n",
              "...                                        ...   \n",
              "1447  9e5ca8416de6011fa8c0d63430e9b89fe751ee2b   \n",
              "1448  98febb989abd1bdb624420f21122c477f2614a08   \n",
              "1449  65efb981347d6f81fb2713cd27cdfdfa9d8781b9   \n",
              "1450  079e5d647ce39cf2ab5b5f37c5ce28b59fb6db13   \n",
              "1451  6263197a62389c9dce0210cd9d65a6e601345edc   \n",
              "\n",
              "                                                    msg  \\\n",
              "0                              KAFKA-1227 New producer!   \n",
              "1     Implement a few of the API suggestions from th...   \n",
              "2     Rename client package from kafka.* to org.apac...   \n",
              "3     trivial fix to add missing license header usin...   \n",
              "4     KAFKA-1261 Make it possible to configure the m...   \n",
              "...                                                 ...   \n",
              "1447  MINOR: Fix kafka-site formatting (#14419)\\n\\nR...   \n",
              "1448  KAFKA-15485: Fix \"this-escape\" compiler warnin...   \n",
              "1449  KAFKA-10199: Do not process when in PARTITIONS...   \n",
              "1450  KAFKA-15326: [8/N] Move consumer interaction o...   \n",
              "1451  KAFKA-15326: [9/N] Start and stop executors an...   \n",
              "\n",
              "                                         critical_files       data  lines  \\\n",
              "0                                  [KafkaProducer.java]  28/1/2014  11720   \n",
              "1                                  [KafkaProducer.java]   4/2/2014   1293   \n",
              "2                                  [KafkaProducer.java]   6/2/2014    803   \n",
              "3                                  [KafkaProducer.java]   7/2/2014   2020   \n",
              "4                                  [KafkaProducer.java]  13/2/2014     67   \n",
              "...                                                 ...        ...    ...   \n",
              "1447                               [StreamsConfig.java]  21/9/2023      2   \n",
              "1448  [KafkaProducer.java, KafkaStreams.java, Stream...  24/9/2023    159   \n",
              "1449                                [StreamThread.java]  26/9/2023     97   \n",
              "1450               [StreamTask.java, StreamThread.java]  26/9/2023    320   \n",
              "1451                                  [StreamTask.java]  2/10/2023    379   \n",
              "\n",
              "      files                                        diff_commit  \\\n",
              "0       121  [{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...   \n",
              "1        38  [{'KafkaProducer.java': '@@ -6,17 +6,22 @@ imp...   \n",
              "2       116  [{'KafkaProducer.java': '@@ -1,4 +1,4 @@\n",
              "-pack...   \n",
              "3       125  [{'KafkaProducer.java': '@@ -1,3 +1,19 @@\n",
              "+/**...   \n",
              "4         2  [{'KafkaProducer.java': '@@ -1,18 +1,14 @@\n",
              " /*...   \n",
              "...     ...                                                ...   \n",
              "1447      1  [{'StreamsConfig.java': '@@ -777,7 +777,7 @@ p...   \n",
              "1448     83  [{'KafkaProducer.java': '@@ -333,7 +333,7 @@ p...   \n",
              "1449      2  [{'StreamThread.java': '@@ -260,6 +260,12 @@ p...   \n",
              "1450     14  [{'StreamTask.java': '@@ -84,6 +84,7 @@ public...   \n",
              "1451      9  [{'StreamTask.java': '@@ -394,6 +394,12 @@ pub...   \n",
              "\n",
              "                                         modified_files  \\\n",
              "0     [build.sbt, BufferExhaustedException.java, Cal...   \n",
              "1     [Callback.java, DefaultPartitioner.java, Kafka...   \n",
              "2     [BufferExhaustedException.java, Callback.java,...   \n",
              "3     [HEADER, BufferExhaustedException.java, Callba...   \n",
              "4             [KafkaProducer.java, ProducerConfig.java]   \n",
              "...                                                 ...   \n",
              "1447                               [StreamsConfig.java]   \n",
              "1448  [AbstractCoordinator.java, DefaultBackgroundTh...   \n",
              "1449         [StreamThread.java, StreamThreadTest.java]   \n",
              "1450  [PartitionGroup.java, ReadOnlyTask.java, Stand...   \n",
              "1451  [suppressions.xml, StreamTask.java, DefaultTas...   \n",
              "\n",
              "                                    dict_modified_files  \n",
              "0     [@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...  \n",
              "1     [@@ -2,14 +2,17 @@ package kafka.clients.produ...  \n",
              "2     [@@ -1,6 +1,6 @@\\n-package kafka.clients.produ...  \n",
              "3     [@@ -1,16 +1,14 @@\\n-Licensed to the Apache So...  \n",
              "4     [@@ -1,18 +1,14 @@\\n /**\\n- * Licensed to the ...  \n",
              "...                                                 ...  \n",
              "1447  [@@ -777,7 +777,7 @@ public class StreamsConfi...  \n",
              "1448  [@@ -1291,7 +1291,7 @@ public abstract class A...  \n",
              "1449  [@@ -260,6 +260,12 @@ public class StreamThrea...  \n",
              "1450  [@@ -70,6 +70,7 @@ public class PartitionGroup...  \n",
              "1451  [@@ -235,7 +235,7 @@\\n               files=\"(E...  \n",
              "\n",
              "[1452 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2999d824-b38c-4196-9426-d9f9929f8d5d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>commits</th>\n",
              "      <th>msg</th>\n",
              "      <th>critical_files</th>\n",
              "      <th>data</th>\n",
              "      <th>lines</th>\n",
              "      <th>files</th>\n",
              "      <th>diff_commit</th>\n",
              "      <th>modified_files</th>\n",
              "      <th>dict_modified_files</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>269d16d3c915d09f650ae32aa81542bd8522ca68</td>\n",
              "      <td>KAFKA-1227 New producer!</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>28/1/2014</td>\n",
              "      <td>11720</td>\n",
              "      <td>121</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...</td>\n",
              "      <td>[build.sbt, BufferExhaustedException.java, Cal...</td>\n",
              "      <td>[@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>253f86e31062fb86401abdc13835c251eef47417</td>\n",
              "      <td>Implement a few of the API suggestions from th...</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>4/2/2014</td>\n",
              "      <td>1293</td>\n",
              "      <td>38</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -6,17 +6,22 @@ imp...</td>\n",
              "      <td>[Callback.java, DefaultPartitioner.java, Kafka...</td>\n",
              "      <td>[@@ -2,14 +2,17 @@ package kafka.clients.produ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>fa6339c19cd06880d32ec9a5ee6b66e7f1488dcf</td>\n",
              "      <td>Rename client package from kafka.* to org.apac...</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>6/2/2014</td>\n",
              "      <td>803</td>\n",
              "      <td>116</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -1,4 +1,4 @@\n",
              "-pack...</td>\n",
              "      <td>[BufferExhaustedException.java, Callback.java,...</td>\n",
              "      <td>[@@ -1,6 +1,6 @@\\n-package kafka.clients.produ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>36eae8f63c81f8d38c2351054c15bf7d5eb612d9</td>\n",
              "      <td>trivial fix to add missing license header usin...</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>7/2/2014</td>\n",
              "      <td>2020</td>\n",
              "      <td>125</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -1,3 +1,19 @@\n",
              "+/**...</td>\n",
              "      <td>[HEADER, BufferExhaustedException.java, Callba...</td>\n",
              "      <td>[@@ -1,16 +1,14 @@\\n-Licensed to the Apache So...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7e154a36f74ad0ea7e0f6d48b71a5a73d99330e7</td>\n",
              "      <td>KAFKA-1261 Make it possible to configure the m...</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>13/2/2014</td>\n",
              "      <td>67</td>\n",
              "      <td>2</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -1,18 +1,14 @@\n",
              " /*...</td>\n",
              "      <td>[KafkaProducer.java, ProducerConfig.java]</td>\n",
              "      <td>[@@ -1,18 +1,14 @@\\n /**\\n- * Licensed to the ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1447</th>\n",
              "      <td>9e5ca8416de6011fa8c0d63430e9b89fe751ee2b</td>\n",
              "      <td>MINOR: Fix kafka-site formatting (#14419)\\n\\nR...</td>\n",
              "      <td>[StreamsConfig.java]</td>\n",
              "      <td>21/9/2023</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>[{'StreamsConfig.java': '@@ -777,7 +777,7 @@ p...</td>\n",
              "      <td>[StreamsConfig.java]</td>\n",
              "      <td>[@@ -777,7 +777,7 @@ public class StreamsConfi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1448</th>\n",
              "      <td>98febb989abd1bdb624420f21122c477f2614a08</td>\n",
              "      <td>KAFKA-15485: Fix \"this-escape\" compiler warnin...</td>\n",
              "      <td>[KafkaProducer.java, KafkaStreams.java, Stream...</td>\n",
              "      <td>24/9/2023</td>\n",
              "      <td>159</td>\n",
              "      <td>83</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -333,7 +333,7 @@ p...</td>\n",
              "      <td>[AbstractCoordinator.java, DefaultBackgroundTh...</td>\n",
              "      <td>[@@ -1291,7 +1291,7 @@ public abstract class A...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1449</th>\n",
              "      <td>65efb981347d6f81fb2713cd27cdfdfa9d8781b9</td>\n",
              "      <td>KAFKA-10199: Do not process when in PARTITIONS...</td>\n",
              "      <td>[StreamThread.java]</td>\n",
              "      <td>26/9/2023</td>\n",
              "      <td>97</td>\n",
              "      <td>2</td>\n",
              "      <td>[{'StreamThread.java': '@@ -260,6 +260,12 @@ p...</td>\n",
              "      <td>[StreamThread.java, StreamThreadTest.java]</td>\n",
              "      <td>[@@ -260,6 +260,12 @@ public class StreamThrea...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1450</th>\n",
              "      <td>079e5d647ce39cf2ab5b5f37c5ce28b59fb6db13</td>\n",
              "      <td>KAFKA-15326: [8/N] Move consumer interaction o...</td>\n",
              "      <td>[StreamTask.java, StreamThread.java]</td>\n",
              "      <td>26/9/2023</td>\n",
              "      <td>320</td>\n",
              "      <td>14</td>\n",
              "      <td>[{'StreamTask.java': '@@ -84,6 +84,7 @@ public...</td>\n",
              "      <td>[PartitionGroup.java, ReadOnlyTask.java, Stand...</td>\n",
              "      <td>[@@ -70,6 +70,7 @@ public class PartitionGroup...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1451</th>\n",
              "      <td>6263197a62389c9dce0210cd9d65a6e601345edc</td>\n",
              "      <td>KAFKA-15326: [9/N] Start and stop executors an...</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>2/10/2023</td>\n",
              "      <td>379</td>\n",
              "      <td>9</td>\n",
              "      <td>[{'StreamTask.java': '@@ -394,6 +394,12 @@ pub...</td>\n",
              "      <td>[suppressions.xml, StreamTask.java, DefaultTas...</td>\n",
              "      <td>[@@ -235,7 +235,7 @@\\n               files=\"(E...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1452 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2999d824-b38c-4196-9426-d9f9929f8d5d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2999d824-b38c-4196-9426-d9f9929f8d5d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2999d824-b38c-4196-9426-d9f9929f8d5d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-01f992a1-3023-4eba-8c33-e1e9fa641380\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-01f992a1-3023-4eba-8c33-e1e9fa641380')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-01f992a1-3023-4eba-8c33-e1e9fa641380 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Conjunto de Commits com Arquivos Críticos (by ATDCodeAnalyzer)"
      ],
      "metadata": {
        "id": "hRgzZfAxjC8O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list_comits_with_critical_files = df_commits_to_analyze_aux.commits.to_list()\n",
        "print('Commits com Arquivos Críticos (by ATDCodeAnalyzer)')\n",
        "print(f'{len(list_comits_with_critical_files)}, {list_comits_with_critical_files}')\n",
        "print('\\n')\n",
        "print('Convert list_comits_with_critical_files para um conjunto de commits')\n",
        "set_comits_with_critical_files = set(list_comits_with_critical_files)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vEJqyYiBjFeJ",
        "outputId": "3a3b65aa-907a-4c50-b281-5ae67f7c2d90"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Commits com Arquivos Críticos (by ATDCodeAnalyzer)\n",
            "1452, ['269d16d3c915d09f650ae32aa81542bd8522ca68', '253f86e31062fb86401abdc13835c251eef47417', 'fa6339c19cd06880d32ec9a5ee6b66e7f1488dcf', '36eae8f63c81f8d38c2351054c15bf7d5eb612d9', '7e154a36f74ad0ea7e0f6d48b71a5a73d99330e7', '3f0b67b6ac864befccfdd4bb5dee08c0b33c3b43', '5e2a9a560d847bd0cf364d86bd6784f70d99c71a', 'f1a53b972eb1f8e75db54d3272d9eb7c398e238a', '5ba48348b3abb8f84fda0798d992ff2e0a04051d', '466a83b78c2bfcb9ac3116748394e7845a99bf7a', '23d7fc470638c4dffa5ca005ef2e3d34c14dc92e', '98952b3cc7bb9b08ad553da014c80b79d0bc24d6', '640f3b05efd00d5e1d7bfc8fe9c90c7aadc3d087', '99f10739b5921534dddc6e3773e4f8a05b8909b0', 'c24740c7b0f6a6e7c66659da786a346650b76766', 'bf83131dffbdb6c39de0135e0426701ca141f150', '02311c0642b3358e8180191ea0542e985ed0f6dc', '548d1ba0939c43fff14531510140e2c641b1caa5', 'f1c6e97d718581566d037a48640ac3d869d1f15a', 'cd3ce27d4baf5434676ec040d64663ad3ce09817', '7062ed7db36f913328a9124797c47f70e3461025', '92d1d4cd31e1045f0e000e8d2d777c73f7529743', '50b734690a93e58030f69cede8d0a84d1e3f5461', '517503db2616531b08ee4d08d39c0e1c0bd19e97', '6f4dea9dbce5cc9f69a917182981b41a56a98a85', '688e38ce45a7358a1e0bb359aa9b1a698a841619', 'a611178408cf8497054ff015caba18cfcff70a60', '0699ff2ce60abb466cab5315977a224f1a70a4da', '1c6d5bbac672cbf49591aed0889510b10e3285fa', '0839def4bdbfd9f5939e463f42cfbc81a98a8eff', '0636928d961a6ceaab46d908f9372d913c3e5faf', '0b92cec1e07a1f2d9aa70f3ecd7d0fb12290d2e2', '01f20e029fbd068a8493492a2c4a1364a5668579', 'b2c833aa41cb9a7a6232781b273402042e021607', 'c62cff355936eea2a96e3b9678c761334fce8ee2', '991195416e0c179d2d2a79891d0214244c287618', '75e1cc8bc497e6aaa0dd05454d6c817ed0fb5e23', 'b1cc72510792fdf83ac2d6f302ea07c7ea397ce4', '761711ecaf9ce8a876defdcaf6802c1e048edc3d', '01e94e2b4a22eb0dd9ec4c3bbc353d054b1904b1', '97358411af15b64a4bc21c58a0e477fbe9c9b79d', '33af0cba3bff87874ae6cef61900cd065edad064', 'd6c45c70fb9773043766446e88370db9709e7995', 'd22987f01d50549d855ae092b69f520d75bfeb7b', '78ba492e3e70fd9db61bc82469371d04a8d6b762', '7009f1d6fffe3866723d1d33a28a4572053eb4e5', 'b6d326b0893e60b350608260fd1bd2542337cb5a', '6d4991e312548bc81e0f6b790a370b6165a92df9', '14e0ce0a47fb7f6ae6dab085b2ea9d5a1f644433', 'a99f70feb23db9ac4274cad9e8cbbf9934d3d075', 'f13dd8024d5bc1c11587a3b539556ea01e2c84ca', '69b451e28944deb162f7427105c3090f41c8797f', '99c0686be2141a0fffe1c55e279370a87ef8c1ea', '4fcb7acafe4c98cd2930e6d348d456a3eb7f7444', 'fd3b4cc41e9249ec6848cde8137691d32b2e79e5', '594b963930c2054199ed54203415d1cb7917df27', 'be82a2afc9e38adc0109dc694834ca5947128877', '907ee7b5eae9e163e7abb24f97dedbaa78bc9d8c', '006b45c7e5b94e70b3a4200f4646042f3557c48e', 'f6373e4d9929d123a8474ab7673ee701f63ac593', '04b0d870b263117e301584bfc00dd8e81486617a', '9e2c683f550b7ae58008d0bcb62238b7a2d89a65', '9a6b57334953ef86603454e5b0d1287ee4281d03', '436b7ddc386eb688ba0f12836710f5e4bcaa06c8', '35eaef7bb4ebcf6b209312db774564451b052ca9', '13c432f7952de27e9bf8cb4adb33a91ae3a4b738', 'ff189fa05ccdacac100f3d15d167dcbe561f57a6', 'f8902981c65ac93426c0edc65d10381d7c285ea1', 'b8b1bca44095df0481fc6bb0d7ea5c06686b9337', 'fd123965802f900fa474a8d5b1539e62d28bff73', '845514d62329be8382e6d02b8041fc858718d534', '65bf3afe86ef883136fcd6bc857724b25e750bd7', 'da39931afad8008bc2b385a75a462777be051435', '9dbeb71ab258955e04b46991c1baf880b07633f4', 'e18f6860c2b297cc2ce06f9f3cb37987af3bdbae', '6ec88f7f8a18be7aff12fb0c73fc8abd00ef62d2', 'd1dd1e902685178a990861f9948fe07f820cf4ef', 'b9ceac3ace353526da9c5d0f5b486265c871793a', 'bcf374da9e0d9ec8d13ecdaf4a1a15691549442b', '48b4d6938dfb9dc72c941e979ff0d0c4d921f743', '7e453df3179efed92d5bbe4ce9ee1d90a07cd503', '263c10ab7c8e8fde9d3566bf59dccaa454ee2605', '2c4e63a899c6cf832fba4be35773218a9ba5239f', '5e769ccd5bf81f82c90069d4b0e79182a1cfe3c0', '37f7d75e3d55c600902cd15c3cb219ddd221d23c', '726e23ef491637e03b0ba90c21992a639dcd158d', '693d4ca1a6fbb883fef5a2e7228ad56dd2f95fc8', '5a921a3239e8b6c6f0fd6a65093c491ac3e539fd', '6a06e22ef81aae3335ca59c94cb0fc33e9f4ceff', '7233858bea1c548153ed00b7edffd55299db7cf2', 'c67ca65889721e3af7527ab26df49a9fb4db87ef', 'b1ce9494e3a964613d3d9534471df79593514c77', 'd8c5750799b9c5c4485514b6b02560679af8255d', '28e59a1df7da9bbfc133180ee332a68e4540ee3e', 'c50d39ea82e282d08163ebe7f0e398de2dc8c128', '7efa12705d91e21eec527ab49033b01ca5e3e3ce', 'e2e5c891428e5f1288b6f4674983d924f8bdb779', '8f32617e6506a5f67d4f922f4088507994ba91ef', '0785feeb0fae2a4f75a59147197c5138109b1b39', '86eb74d9236c586af5889fe79f4b9e066c9c2af3', 'aa56dfb9e7cea19faa545a13d42d499a6958cbef', '71399ffe4c52e2539a5794a17852c8c5b3d5fe72', 'b251bebbcc3074f0491fce988f1d46ccdc97f1fa', 'e6f9b9e473f0396743cdfc5236bfd551316fc6f7', '38a1b605533632f61f4c23b69933eb496098311b', 'af42c37899e8fc66590e7f3c4893f8224441f6a8', '13c3e049fbf22522c90c2a0b4b4f680b974d9bea', '1ac2640f8095262f423c770060b737f81652e211', '443cd9ab0db5223b7c85ac8333f2fc1a0e554870', '1b5687b9e4cd10e0f91fb921d3569fdd44be163d', '758272267c811bf559336ea45571bc420a62a478', 'e466ccd711ae00c5bb046c18aacf353b1a460dcd', '7c33475274cb6e65a8e8d907e7fef6e56bc8c8e6', '596c203af1f33360c04f4be7c466310d11343f78', '421de0a3f93f8d21f7ca0f1287a4305c00edaa08', 'c39e79bb5af5b4e56bec358f8ec3758e6822dbcf', 'ef5d168cc8f10ad4f0efe9df4cbe849a4b35496e', '76bcccd61f61d33a4a4d4db02043a2f0063d0d38', '23cc9c77b27889bb7d71d7ea74b80ab96c4529ae', 'c9264b4c8904bf330b70cd84b433a7a141ec9d0e', 'bce664b42a0c414dc19e2c07406cf6f14890cbd1', 'ae5a5d7c08bb634576a414f6f2864c5b8a7e58a3', '9d9bb708bf59c93672306cd731b89d7df114bba7', 'e0098b4567541d1c0cefc6f57ce38f67a9133b5e', '124f73b1747a574982e9ca491712e6758ddbacea', '45e7f71309f9a3e30d25a6ddd3171c67e3e79286', '4a3d244a2cb95654d18368de6e5b67661d4f4f10', '1a36af80b79a634a652e594d8240924ac32376ae', '015862348002cc6d1148dda0555fe88c27998982', 'fa4244745fc363410fd5bc21e0b045f8124a8f9c', 'c213952fa180c56908ef70c01a784d24202beb55', '617a91a236a44431aeca9345ae954f7067da48f3', 'e14ae66e721f98ca7c18d35ea022e6de733f4c03', 'd07bb1814010ca4d822e44330d1e8ea4b2839c80', 'c18a1bd64e7f8416e8256b968fe1b9e2da553b00', '1dd558aeceffdd98da83f8240cdc3bf8bc182c93', 'ea971397c8c1b8deedc48c4165aded42118eee43', '9fb1e25738f89b75a36ef69f730b0e138ccd55b1', '13e483adeee8d968397a21bde3bb159516f26ff0', 'cd54fc8816964f5a56469075c75c567e777b9656', '39c3512eceedebcb6e50f8c6c4ef66601ff7dbc4', '23f36c5903e9b65c2b322a9405149de671de7a23', 'd05fa0a03bc9bcfcff8d73cbf1b22832ebdb75a2', 'ef92a8ae7479560b26edecfa8db79934065f13cf', '991aad23baa2f55d405d374b0a01785acdc63974', 'ec466d358dcd5ff9e035a6d5de82ada12d187ae0', '9d23b512ccb95940782f51c93bd6cdbed3e938db', 'fd8af25d0ae354e962850c9d6b5991056613aa2d', 'e08b922aad485a96fceb51d5c27877729156ddab', '3f3358b6d4374662f5ca57c6e93e009b58a6b2a2', '841d2d1a26af94ec95c480dbf2453f9c7d28c2f7', 'a2a417caf9fa4f177e099fed7d2a9b8d1942a9d6', 'a0d21407cbdd3b7cb73f52c986aa2f60804618e7', '5aad4999d1a1d35d61365ff57a9b79a6af3e70d2', '40d731b8712950122915795acca43886851a73b6', '7059158c48ec05288c0b45ccac05d62b38f1bf76', '4f22705c7d0c8e8cab68883e76f554439341e34a', '37be6d98da842512367ab0b31d8f0244afafda92', 'a62eb5993f5517a64dd1020b0a9bbd1012f7ee67', 'f75e3350259c6df11719ee0a55f5780c136f3d26', '959cf09e8653f4b8255f49c6f4c258ed1a5ec38e', '21c6cfe50dbe818a392c28f48ce8891f7f99aaf6', 'c197113a9c04e2f6c2d1a72161c0d40d5804490e', '5ae97196ae149e58f6cfa3c5b6d968cbd7cb6787', '82c219149027e8d96840af98d32fb1b877ab4ec2', '57da044a991ebf8913d44dfcfa6a27729f54a4d5', '4adfd7960c4bef187454d1ff5186f3be690abbf5', '95174337c2f6cda90e213e5c3a73fc89854f42a7', '79eacf6c95506d5d6819add5a1256681b13170b1', 'db8d6f02c092c42f2402b7e2587c1b28d330bf83', '4ee68b43c180d1f68648c0fb388a66b1ed0023e5', '0eaede4dc95846e2b8f7452f41c58c0122e7a563', '6352a30f46f2da11a8dc3e58912d0a2db8284c35', 'd5b43b19bb06e9cdc606312c8bcf87ed267daf44', 'c1f8f689af43f5ce5a95dad86537db4615449694', '67a7ea9d6744645dd4e08b6a78dd69704a4982b3', 'f141e647a4a3daf618b48073058320ecaa3671d0', 'eee95228fabe1643baa016a2d49fb0a9fe2c66bd', '45c8195fa14c766b200c720f316836dbb84e9d8b', 'ff7b0f5b467bdf553584fb253b00f460dfbe8943', '68af16ac15e5675daebb710ed8f15f780dc43abd', '878b78acb68d0fa62eb3c8f21d572664ca2bcdd0', 'f7fe9ccb466f85c417a545eb8acc4d61047c9f09', '73ecd7a1797a023039fea365a08dbdba4171d10b', '13b8fb295c3becc27e7954af623fe90b3062409e', '5db2c99e15a4467489b495144815ac8334c2c5d4', 'd51dd5fc7475ad29bea8616f77e1183a8fa9e000', 'd4e60b9f59e04e82125b404f173c4fccc949d906', '845c6eae1f6c6bcf117f5baa53bb19b4611c0528', '5da935ef78ecf4a671bddc632cfd36ef65bd6f6d', '79662cc7cb139c33888148139f44fb4b1ac0fe56', '002b377dad9c956cd0ae0597981f29698883b6d5', '2a58ba9fd893979f89aec251579b10f5cda41d10', 'f676cfeb83195f032e3ad8908288a7e04011c830', '33f33075468f53bf6d4d1bd79fdb64443ca30dfa', '25bde48e6a43b0a2c1c9430eb560b43e160ae3ea', '5afa1660103df8aecbf7558e761418944fb5905a', 'e403b3c4bf8ca308fe180b093da20700f4db73c5', '764d8ca9eb0aba6099ba289a10f437e72b53ffec', 'c1a56c6839e77f3de5266315a92b236a379ec857', '241c3ebb2803f1e09306fb06f20a66e7a60ca3c8', '355076cd262dd071287a1c8586ae0f9635e218e3', '9a836d0154efe6ea1effc688567186cb56265bf4', '958e10c87ce293c3bf59bb9840eaaae915eff25e', '30e78fa00650b258f3ab5ef6c9bdf5ca137289c0', 'dea0719e99211684775780f5da8b93835d7a5dac', '5d0cd7667f7e584f05ab4e76ed139fbafa81e042', '4332175c11dda5deb491f27a6ecf66661676ca47', '8d8e3aaa6172d314230a8d61e6892e9c09dc45b6', '99cfb99aa161007f2ceae33945d11b968d932175', 'd6b450ced57bb5ab8a7164358e6a360f97a7b5cc', 'cb78223bf90aca4f75699f36c1a82db7661a62f3', 'c1d8c38345e0a1e04ced143ed07e63fe02ceb8b0', '23b50093f4100ce7fbff325cdc92ee6cf3c54102', '2788f2dc73dd62c35c87842d02d23a0ed6a62e97', 'bd5325dd8be7c5cf920acee2aa33b3c288bd551a', 'dd5480a47eb0f45214c179b7f14ffaf493164222', 'ef3f053155c73af053a36a2e50047c5a49cd30bf', '99d232922734e115f8e3585d91ccfb0ef067385d', '8dbd688b1617968329087317fa6bde8b8df0392e', '9c34df1511a769b272893b75ec1ed90d38cc9576', '3a58407e2e0aee0bb2c5e343fbe98c7f28fc6f3e', '9beafae23a83774fc1d9ea811d449eac34240363', '7c27989860e7e063bfec3c679cb6bc8fd52abc84', '1ec842a3ea9bbe414a5c59f90569fbc3348bdfb8', '40fd456649b5df29d030da46865b5e7e0ca6db15', '319c6e7195143c1fa4a1e5b34ad923c2b73f373d', 'ba9456de2ebf1a34bdf5f6f62a701875822e1923', 'b1691cf49e9de850ac8a2675c487af9fb60bfdaa', '8a863ecee7e5dcdaf66a55b91040a7893ffdbf66', '9d71489ff0e62fd1897c7a6f54671673db7b37b8', '996e29cfe8a9e5a45d4b778a84fb20479eeba469', '18dd1986bd354e8c7d75982112fad848780a6b33', '2790d0cb1fc6ade216f2c1bf2667a0ebb53d85e3', '33d745e2dcfa7a9cac90af5594903330ad774cd2', '4c76b5fa6a72412efa5936c284800148c2c69c24', '4ab4e4af814fb791fe6e8c2bd3381da8ca80b0b5', 'fe6c481b38d6b1b61341e4e1a6237f64accfbfbc', 'af013788436ae5652b531184770f2620f8914123', 'c8c6ac3f6d2d590261acb35fabbf7418ae102d4e', 'ad316509787787afeed6e2a24a62fd22cadd09c7', '32bf83e5a792c5ee9eb88660da71b73aad5bbc02', '2ff955044aa875176aaa58a9be4a79c494a3fb27', 'fec2d41bc2bed9f4effbd9922ca487bb02e5eeae', '5aedde5b29cc5a1adea4c65d5c44fb657f162eab', 'f34164eed53d791768f05df21f4dfeca89859b2e', '53fd22a76613b309b7941a5b0c64f17523b39202', 'ab8fc86382fc9f5a8a0f696a91c66806d51c05a5', 'd5366471d2c1a611f2241362041debc0118e5422', 'f79db1a4234b4af75a2d027c86df62d77ba3d29e', '107205a7f17a4b1e235089668cb283bf16fbb88a', '245fa2bd8c08bb17c0b2b7aad4aba145d3c49783', '7699b91f870f58e71dbdeddba79b58f009f9c0a2', '0cee0c321897b4fca4409651fdf28188870cb2f0', 'b8ea094b427768bb360d87fc0a07f670cb667e1e', 'fb42558e2500835722a4e5028896ddae4f407d6f', '7d9d1cb2355e33270703280ed6bb712033b03d26', '47f5ae9e9c902e18fc0f7f153da945de2839c3e2', '32e24b7c28e357c051bbe21b08c4e8c6d45a3907', 'f1323d4ffa451b8c7965d51f422f0933f35ff828', 'd7de59a579af5ba4ecb1aec8fed84054f8b86443', '12fa188b9aaeafa337c813f4a84a3117d6bf403f', '416c3a7d97d473d0d9928c814d68d502c21077b7', 'b64302f9cea0e3e95d740cbe7bbd4c64e147301c', 'c439268224e3178002bfa28bc048722870f992e3', '98dfc4b307c2e41a7fb0fff330048aa9ff78addd', '9f94a7752a590c36186a7eb1eee16e992ec68c97', 'fbc5185543fd4895c7c81ff55b3c8b4c25ac7600', 'f1dd0d272313deceaf7021e54c09a63043acf4af', '14934157df7aaf5e9c37a302ef9fd9317b95efa4', '9991086671b0a74bc5fd5698ba81aaf9a2985404', '933a7506efb57add31a1e9dbd984c1230128b855', '8deedcacb68ff8ebae802e086ff706ad7e5f38e9', 'ff557f02ac628edbe220ea69888d39de834527d3', '131f96868aa24f38f69ee2ab769ea3f289e5c1fc', 'bb629f2243c4462db2a863793c190d734f11f3c6', 'e62dd4cb770e3662524d4f2f50547549e3ab2eaf', '416817920bcc0a5f74ddf5231505160a68c7d2db', 'fb65ff40a8d89bc7ad9b079cf1f2a8b902abd182', 'caa9bd0fcd2fab4758791408e2b145532153910e', '68b5d014fa3bf2e18da253ded2bbcd4f5d4a9d8d', 'be36b322749003581474e2c84a3ec9ba2aaec53c', 'ae237be1bbfd7d1b84a5095491a6131cd3cc9346', '40b1dd3f495a59abef8a0cba5450526994c92c04', '317c4fdede41f2026b34f473af1ad69f8ee62a1d', 'a2bac70a6634e9a78734d23158fb7e45f290ea26', 'bc397db0e13998160f0b4e723211d6662ca7e964', 'ed639e8263d9409a9836a55938174122d6ff3ffa', 'd2acd676c3eb0c11d0042bc3b9ae314165c68443', 'eba0ede878bd9c42431cc702bc58f56354c595a6', '69ebf6f7be2fc0e471ebd5b7a166468017ff2651', '54767bbba5bf18c01f50bb40c339433bfed09627', '4af50bb8600c37ee2e3597fba9a54a29cef94afa', '2a660f13d2a805f0e27351996e904b1ea2365eba', '084a19e9acb43666cfcaa2ca155a775d47cd8b39', '86aa0eb0f274c6e44eb190ce250433419e011a67', 'ecc1fb10fad39c2da82731a36552cb5dd9ac2858', 'd04b0998c043a6a430921585ffd4c42572a3bf5a', '3d74196f205c53946b6fc3dd0501aa2095f0031a', '4e1c7d844f743e5b439447e645fa41d2f92b8b5f', 'cf8f4a713b64f010c15a4a7b8dae616edb8a1b74', 'eaaa433fc97b86450833d8fcc8c9289ea35d47c0', 'a208478f37f216324a87b530f18f30ecd79601c8', '0c4cc5a4466d0dfce01903c29d97af3e428597f3', 'd83cde7cabe4e86951c6760e68e65b99752cfe0e', 'aa506a6919e8ec0381657db2c2f15dc1f62b00f6', '20322446aa261dec8b51e6e4514307e926a29ba5', 'f8b69aacd4ed1af1263e46b93879cea2d855df3b', '8124f6e0996cb673760750b3aba004ae11e34c6a', 'c78a3b173d019e8bb58f4e31c94bf4dd3f7ab614', 'a23859e5686bf93ed7e0d310f949757694d47a1b', '7115c66aefb11efa802e61a42bcc13fadf92598d', 'c9da62794547ee75a7e2ade1aa8bbf4a1443c49c', '454f6845b34d0e3faabd9427f3cd8bbe6209c30d', 'e876df8b37fc6ea54b0a0571306c4a833c919cda', '6b91f83fbaef26fefb9ec221529c29872a04c004', '1a67739c2f3d0418a8f2d8f7b15d5af2ed3a324a', 'e7663a306f40e9fcbc3096d17fb0f99fa3d11d1d', '6199c62776bf3ce9467703ca651a0119b261e60e', '925310aac0b6a0fb32e3e2d614198ffc78f34f96', 'b51002c576ea9758132d75a8a0fe454e1bc270a2', '62c0972efc525cc0677bd3fd470bd9fbbd70b004', 'd092673838173d9dedbf5acf3f4e2cd8c736294f', 'a4ab9d02a22e77f0ebca0450e608898d83d4fe18', '29ea4b0f1481aa2ee29694f2b469c16059f9c17e', 'ba322e54203ffec5779944753f264d70b87a24fd', '5e7190cf83860190d54815aab957a58c26fd816e', '471a2ab17df66c52e0a98afa81e0b360560a5e9a', '11766ad31861e0350178f59127e471fde2b20970', '7c36fc377650dac8a9d6e5f2d0049a78cd93d2e5', 'a66ca1624c692d30b7ae1e25c29915331cbf41e3', '7282de39f7b7cef1ca85f21771f2401a4a67eaec', '3b4c347949c02b1e2b1dd473deda0f8d2304d027', '2daa10d77f8f177a8db6ff5de5c511165fedc2f5', 'e53babab9cada20cc54a18c0fd63aa5ab84fd012', '44f61ba4f96df995265f9cfa004aa49c82b02374', '1503f7603c1d5e0511bf71175d84bde555c80e9a', 'f9d7808bab52a0a6fa879aaac0b1da80e0f33adb', 'ea42d65354b5905668d45dedae1cd1f7f39c888c', '128d0ff91d84a3a1f5a5237133f9ec01caf18d66', '93804d50ffc40fb6cdc61c073f4a62f0931f042d', 'a4592a18641f84a1983c5fe7e697a8b0ab43eb25', '36528df088a28b4373f148627627d7c116297209', '7f8edbc8e848d22b1cb016a7208bee1e52a65b73', '9bed8fbcfc52ced719f2dcafa3f30cbfd5e6bd57', '7c7becd4cb88b372d199b05661f8efb1e1d022e6', '6f7ed15dad8d914ae65a595eef327c0510f11469', 'efeaf129890c2195b4753d5b9eece4f1b7cdf756', '67f1e5b91bf073151ff57d5d656693e385726697', '859113786957a36381222f21287a940767e92f1c', 'ea724497a85e922af6acccd02d7e7466871fb339', '056ed86600ebf884057ea24daafc9e3b1ab85b81', 'b58b6a1bef0ecdc2107a415e222af099fcd9bce3', 'd6b0b520fc0755fa560af84f5fc4c9f86699739c', '56c61745deeadd299e5ef24577c1bdf5d9d231a0', 'cea2a669b34b2f94d445aace2b6e95e70af7e2f6', 'b565dd7eb184da5ef3b08c88a8acc3df221aaa08', 'ac57d1751186967bcba6aceffa6857a4449fa70c', '109aa353686cc0a9e4448c2b48da63f1d79acae6', '54e2bd210029b124be4835d5c50c56aa5edc5b38', 'a95170f822227c50414c57860e8547dc2e9d84cb', '4b71c0bdc1acf244e3c96fa809a1a0e48471d586', '3d60f1e64350710a5fd2a6036da353cc83195741', 'ecff8544dd45e8cf0fcf04f5e0e716d3e21c9f20', 'f72203ee9223d3b724ee67bdad9912612dd72f63', '23dff4b04a4c4cd338b5f39c9bb7d384857c482c', '8079c980ac5c9c00d7577fa05857b4de6864dbb5', 'b6011918fbc36bfaa465bdcc750e2435985d9101', '2eb32d6a70bcdef86bf87f9afd56b70de9df505e', '6496271a11003195d703292a68c6a6c04e56d79d', 'f6fee34a2d6315cd25855b9805e2f4129e24e5c3', '88fdca26a0881d12e9f3ba942cd580c8e8154025', 'c2d9b95f36a9bb853bb3851696161d7f7f790bec', 'b712b8675d6977a9171edc6569cf382ffcd91354', '813897a00653351710d37acbbb598235e86db824', '20e957c9b1f389621bb8ae9e9f5554b66cd92a4f', '3400d0c3cc07d5d3136bf9a19142b36cca93f92d', '79987590e3e96351ff75ce86718801ec605b2419', '0971e62e50b94a74aae170fb49d1e532e39b2136', '135488352cbe646334a274418d7014a91e92057f', '5b4e299a46a8ee92e450c54880408713606e4f52', '31716590454e1bdd53a65836741f316bf4715c9d', 'ca0c071c108c9fd31a759e1cd1c4f89bdc5ac47e', '89fb02aa81fe67f5a506f579e463f30f3020dee5', '4c42654b1eecebae272dfe5ce018b85ad4db7709', '8827a5b34ed2a2a570219fb8f29d955918867796', '413444c0ec01ea51019df436cc1f2b664ac61e5a', '5afe959647fcad9d01365427f4b455e1586b1fd5', '0b48ea1c81f22465cf32a19c012e0fb3c849afcc', '7de22453bbd05a7ac629543e09626ac6987ce306', 'b31a7a9022bfb645cb61e6fbc0b22829108b3a3f', 'b621703706118b383153da60937c1d31d518d5b8', 'c7c113af63d8b4c49f16297af22b3a02528a500c', '9898d665d1ab201405d66c70e3ea9710d9dcecd7', '253dfcf4756e8e0d434da69516993ea784f830c9', '4db048d61206bc6efbd143d6293216b7cb4b86c5', 'cb725c6a2d21651fe8b2e50225635209aa949bb5', 'fc1cfe475e8ae8458d8ddf119ce18d0c64653a70', '1f1e794ad0025bb2a33b7d8378a481f224b3bccc', '015f1d73811e52b3b7a578cf4c1ca4c48b6c2f5c', '3428bc1a2549778378ffeb781ad62ca719ca3b7f', '1ed4b48d5a43af743229fafc22f78846c9c3134d', '0be3a7226f816bd4b612477ea3b9916bddade290', '151b23689ea1c2e13a1d38f403bf5839eddbe377', '3749832637b58b291a45076658feb8cd6e830247', '9260e8563c19cc1c5ac2d47ad05955774138036d', 'd0e436c471ba4122ddcc0f7a1624546f97c4a517', 'de05c9d3a0d79a555088afe9344b52e002d287f2', 'ef92bb4e00da10728cf74c2d81f8f2bbec4c9c02', 'ca06862a7005ca476f900bd9c2373021422d695b', '670980ef29fb70838dcd462d11789c2519981164', 'a3c45b0c9263ca4481b1af7af3f7d8ae8cb5d5cd', 'adb70da13e18eb652e734887b430ac0ecbc5f9e6', '29084a9b27b959532813141591ce03be2921e860', '022bf129518e33e165f9ceefc4ab9e622952d3bd', '046519d0714591785cd984ab2d57e193b54a2f19', 'b9f812491f5eb06ffe5b68f5e53df4302d2f68a8', '5a2fcdd6d480e9f003cc49a59d5952ba4c515a71', '197a5d5a6d160e9f3f1642caf85bad968a35f109', '57278aa82da5dc9d040eb3dcf4a182e0731a621b', '5bd06f1d542e6b588a1d402d059bc24690017d32', '1abed91bd2915226cf6320395e1a5877ea0705d7', 'f3f9a9eafb9aee9d87dd188521a4fd72466abfeb', '8b05ad406d4cba6a75d1683b6d8699c3ab28f9d6', 'a0b8e435c9419a9402d08408260bea0c1d95cff0', '84a14fec299749a208251bce1a0eb9c1a8241d08', 'f9772d5fb26b0a375729a88db5e7ee199f85c8b3', 'dd71e4a8d830c9de40b5ec3f987f60a1d2f26b39', '5f88cf79fb51996e77614aafdfbb15e4989e159b', 'bdf4cba047334943aa8357585c4fb379b27e9ffd', '3364f12bc240e3fefa6a467519ef608fa768917c', '49f80b2360ab55274ad28405352596f9d560ec1c', 'aea14651184476940c69238535de5143e61f4c31', '97e61d4ae2feaf0551e75fa8cdd041f49f42a9a5', 'afeadbef50ee8cb5c23de26c1b2a5ad2c7ad941e', '2f4f3b957d753debd8d10c98a2c867211064503e', 'e19bf3f24466cd8dcd6c11f2d7cbc5796600fb88', '749e9e14c7a1b8a97f79a2d852d8c8647b55b38e', 'd0e7c6b9304b23ced046934c799df0cba39c28e5', '0be835dde50611277504a4ddd93cc62f39e6defe', '148f8c25453e453f56ef429f8ef607de808de679', '17ce2a7307222d7476eb60318fab2e672eebe559', '98d62a880d91ac1b552ac3a049c5d202c2d63520', '49230625736d30cb4d0467ac0a103b23de5c266b', 'cba0094f5a9833194bf2a13965eda678bd4a05ea', 'f7b7b4745541a576eb0219468263487b07bac959', 'f69d94158ce72f4ef2df5b98f2395c2b2e61251e', 'a82f194b21a6af2f52e36e55e2c6adcdba942c08', '5b5efd4b5cb092813d878b080e0657f40fc1f72c', '36aef1c2682169571b410a4986680361db5eec01', '6753af270c8a966411f5574afa57f6e5ffe43dbe', 'a931e9954d27f4c9f12fd89afd6f0fe523cf35e8', '6185bc0276c03075022c30d3c36f7f5c09ef19c6', '67f1f4d270c279480cea5e6120ca157637910cba', 'a3952aee49c5eb11cd38c1bc2fb95e9a285ff158', 'ec9e4eafa406fec897713310bafdedf6bbb3c0c5', '715eae6da92d5d1db7ac65e3c8ff753e15b5c0ca', 'e71dce89c0da50f3eccc47d0fc050c92d5a99b88', '2554a8dd4dd07b0ac844839b51533bd1e67eed85', '3bcadbfb474f6caccc939fb3775a6f969d136af7', '911c768bcc3ba950d63536ce4e6ba2542542d573', '1c2bbaa501c2d0fd4db9c2dacacc3ff7f5236d3d', '7baa58d797126b6fb2b1de30e72428895d2bcb40', 'a1c8e7d941ad9c765dac232435a297f905eeeed5', '9198467eb293435385edcef9028a59fa41a3828a', '0e2c90bad934b276610fdcf027252553208099ac', 'd66e7af6526f208900a5d6cb588cf47058800804', 'ebc7f7caaeb47c9588d79a2f3ed496daa0bd39e5', '6910baf548ebad4c1530432e51be40793b4a4f10', 'c1fdf575deed0398e1692be16cacf24308afd5d5', 'abe699176babe4f065b67b2b72d20daa0a2e46a1', '8b04d8ba071fbcafc72a17d4cbfe6f00613e59b3', '495836a4a27c2219f72a5f3776ba8e5216493b41', '4d89db968257c0c3273ece7e5546cb95ddcbeb46', '38f6cae9e879baa35c5dbc5829bf09ecd59930c2', 'cea319a4ad9c55d3d3263cf7a4224c25772d0e11', '64fc1a7cae348fad10e84c5ebc457c2a391573ee', '73ca0d215ead9574487744eb89f7ae677a9e13ea', '37433638271718344498d695d5da08db12c24eed', 'faa1803aa35871e5e040a22b7fcec61a2be16e24', '0bc4f75eedf14af6d0b2e3e9be62a460b1049d0b', 'c060c482855bb9f51e19dde51911645413d85260', 'd41cf1b77819ede5716b31683d0137eb60cb7bfb', 'd082563907103ea79eed681305df7093053f52ec', 'dd8cdb79d356b3049c21bf00613b8144dff0882c', 'aebba89a2b9b5ea6a7cab2599555232ef3fe21ad', 'd9479275fdacd172a2b545007b55e787c53fe54e', '7311dcbc5363d59dd0a0de86518dd12a7b259ca4', '681c6fc1fb37c9cfec00df09fdfe5bf1b11f3b4b', '6360e04e70b8f5ce562c2f24f85208aaae788363', '39c1e6259c8ff547c3f7c5165b43c88f9f3ea158', 'b3036c5861df2c5fc8a06ca5b817d43147f5ba92', '0c3e466eb035859659ce41404f3b71b577467dca', '1188db5657e1db6944b59b1c91a75e47856f4e5c', '0a8b10e27f573612e1d5d6787b2096d3a8528b94', '3557f097b81edd6518100d79b442223fadf9f81f', 'cb78ba1294d1a27b2c4d842a125486ffec593d98', 'bb914a0445549b41fb0b667ea2f5f15a90060133', 'dcbdce31ba525771016be5be4abc4a2067e0890b', 'ba07d828c545d542cc11db60249e660d88a20fbb', 'aea53108a7d9573b8976da83235639a981c09af3', '4660bac4eb45a65767d05b03d275e3bf08a85c2a', 'aaca1b478fcce99860e2eb89aa468ff863458572', '160933bc092b4392cac1dd9497010320c6ae4d10', '65e36895f5cf91afcf785857ebff4c0fce8d1d9c', '2572637121527f8c37bc95212e7eaf8190eb436b', '1d556e471411b3ffa5bf65afd08d1a78ee40e710', '54a3718a900a5286baf2193713ea8d58ca2c08f6', 'f49697a2796540452a21d4a29ab879ba04214046', '11aa110126d641cb8f078162fd87bf519ef29766', 'b836bd18f99e32ee71e30693853518fdc2061218', 'ea2a21dda4ee402cf06e127609456b11c5997d37', '254add953477df65fe36144dc0714e0c9815c767', 'cb5e1f0a40e9a9779a5dcabf555a593363728b33', 'fc58ac594f0eb63e0928374f67eba25bfa18eaea', '4baca9172dd662c2e45cd99c638a052eeca688c5', '9ada0f81695d99587b18bffe939de651065076ab', 'd5e463b9de1f92b0dcc8fea4cf74a0eaca835ef7', '2265834803649d2f9fb090e86d11309f6e9789cb', '031da889bc811200da67568c5779760dcb006238', 'efb060c57f05d1d586bb14c016b0187c60f8e994', '0dfeb31a116d26023540f6c6074127f0aab00b6d', '70e949d522eba72b22c2619c4fde372d0f1a26b3', '9078554232f9de56849b5c35bf45b81a67a748c4', 'a1f97c8dc4edd49c7a8a2ebb72734728029a85aa', '2628695356de87aba7d046855fbe1f1963d3f911', 'f87d58b796977fdaefb089d17cb30b2071cd4485', 'fe4a469fce5a47b89083781ed8a03d0d71428aeb', 'e0099e1f5852536918bfa27b4d1c33779e45ab6b', '5bb53e034e4f8a06550dd06377fae7b3c2137ce2', '1110f66fa7278212160c6e8392b6e6ab09560452', 'fc93fb4b6116e809f2b69ddb2f7e0f12548fef51', '5d798511b12c5ef7555e4234fdd99a360176e435', '89faed8d30eb441fac7f1564edd40115006f5051', '9f3f8b4de64501902a4843064b1cf1feb21c690a', 'f8498ec9e27ca0f08e3791d7a19ad8c6a97e210f', '9d6020ec5fac21f36e2caa58256ee2158658866e', 'c50c941af1395d010160790f0127f06106cc69c8', '1844bf2b2f4cdf5a8209d7ceccb6701fc7dcf768', 'da2205578be3228ce40eb5e59f6bbcb0c8da0aba', 'cd4c6c0183edfbf8e7e8ce526d638e31fcd09186', '228a4fdb6dc15d1b8615615e00825e7ce53a41fa', 'b7684b47b80a2a7d604632bb325fde49e570480f', '125d69caee993710bc07523fdd3deb0e06b10636', '6bee1e9e5799398550e1897bcef6c5067452500b', '3e69ce80157eb6a5e6dd05e3be522b5208a41bc5', 'f4dc5ac15ca120d8240ac3b8ba28139ccb26cf91', '334a30bcfffc3759aeded08f2089cea4ed6e9937', '6896f1ddb7650f42630aef8c67c8b61866e9fc00', '343a8ef99cbc23e27ada8eb5f8c1b752067a7e41', 'b78d7ba5d58fa77b831244444eef005a62883f9b', '71417552c7651a5b41e73460d0577615864aafe8', '607c3c21f652a8f911d1efb8374d2eec313a4e2d', 'b01fcc3b89ee27940ca20dea00226d09a0ca0b8e', '1f659913a631d9e77e7cd9b9c9775e3249ef803a', '6682abe4ae68fdbf0eb362e45f43ea14e2aba847', '9836adc732288924813927c4869ffb3908febba1', '14a7c297ab917efb28e579fa942d58cdcf4930c5', 'facd2c5a8c7a4379755955ba3e318bfecde6af39', '752e5317410f05d08f69f302e4ee0cadae183b59', '2fb5664bf4591f3e7bdc02894b9de392bf72913c', 'b687c068008a81fad390c80da289249cc04b3efb', '45394d52c1ba566178c57897297a3ea31379f957', '23d01c805bef7504abfa83ecac7e384d121a583a', '3f155eaa23c6081e4afa3b49f0f8a65a16b8e05c', '9b85cf9ed02d4b3b87ea39e0c2f8c35e1e813f2f', '667cd60dc6ba68831423a256b6e455f7d955581c', 'a1ec6527db808679330a54e802c4ae1630e6b50d', 'd0ee6ed36baf702fa24dac8ae31f45fc27324d89', '4769e3d92acdc6036f1f834c70004f0c867ae582', '779714c08bc16fcdd6fe7c39e92a7f73ebebdb71', '9bb06c3eef3ebdff77e5e3b98c26b86c40efc56c', 'd6fabb011af848ff96691b5deb97c88a84e1a683', 'c42bfc0d51e6691b4b5672ef7c8a1bedcd452d7f', 'a0ad9f156a122e3e3a073cba0e2b0c39c93c7db5', '2656659e0d7c0e427768ce216df2698acc8c9b11', '8a5e86660593eab49c64fdfb5ef090634ae5ae06', '5d2422258cb975a137a42a4e08f03573c49a387e', 'f305dd68f6524abc25c4ed88983f0e78b4e6c243', 'd83252ebaeeca5bf19584908d95b424beb31b12e', 'be6252d8ebdf9cf2d151028a7ba20eb1591b5961', '4ac7c48e91f37471e83cc82d26b26e5b715e8e80', 'c96f89f845f790d8e7bce45aae6c8c4d15a25660', '80e0af50d94ffb99f669e55581d262db5f6dd027', 'bd0146d984dd5df82fb19aa936e8f4ff9ca40030', 'bb9859720bf88732cb63ec27cfa10d510d767d2b', '69d2a177101eb1c29b59b4c64d8c22f6d5e3d281', 'c8c6ab63248639b167350642efdfc4341fa3ce37', 'b8be86b80543e41fd3181a8de8f1a3ac0a72e4c5', '8e0e2a5b238092a3730d22d36046fe078a01c15b', 'e1543a5a8ecbd9da6e39fb0952b1193450b3c931', 'e5f2471c548fc490a42dd0321bcf7fcdd4ddc52d', '071f9fe1a2fd903336606f9fae735d7f33d2768c', '021d8a8e9698dce454e0e801092460b98f0a8a4d', 'eaabb6cd0173c4f6854eb5da39194a7e3fc0162c', 'b79b179716b5f8bacb870a53a5a9216a0687b3c9', '3107a6c5c8d1358b8e705c5d5a16b7441d2225a6', '3dcbbf703017985c9e212ad69cc7afdddbf358eb', '42b356500b7188eb2507f9b48399d5491a7eff16', '713a67fddaec3fa9cd7cce53dd6fef5ab6e0cdab', '10cd98cc894b88c5d1e24fc54c66361ad9914df2', '23a014052d39521a3af471b3f95809c2164820f7', 'a1ea5360631f4366ba536fca37d9f906b0e58a5c', '7d6ca52a2751908c7fc6b752d70dfaaaaa9bbe8c', '31fe1f98e2f19074360e85c8ec839356238f0b29', 'ef4914520019e941827dac8eda6000a82cb74cc5', '7a14663929ba1111ba05b57e47cf96fc55891a36', 'f931febc8c4b74baa522a593f16df84a0b05bd99', '7fdafda9796fc8033c5f77bf19be17be462e1b4e', '561dd3864d89db58bd3cbdd64480f12fa7249f1f', 'c216adb4bbf8306977380a1ec371380e30137765', 'b281015c150cb95c4205ff6a8d8945b86fc94d4b', 'c504b22841b1066c0d46183adb22f48596479d7e', '7fe88e8bd9acedcb74a4b3e61440f61481b60711', 'c7ab3efcbe5d34c28e19a5a6a59962c2abfd2235', '86062e9a78dccad74e012f11755025512ad5cf63', 'd637ad0dafb727bb73c63f1b187d9f83abcd4ec1', '40fd9fa98105111bca373e76f8f39914cade709b', 'e3c32391f95d82a217295c7e4c1519981124bc3f', '5df1eee7d689e18ac2f7b74410e7a30159d3afdc', 'b512cd474c6ebfbbbe0192764cec3413a94baba0', '38c5d7fba7387b797a10c9c6ed71bf99c6d417bc', 'd1e0443bac769c10598bbdeddf6d7e06f2fdf7e4', '4b8a29f12a142d02be0b64eac71975b6a129d04a', '234ec8a8af76bfb7874dd99714a65089d6048953', '043951753b6fb6c8bae6d25d7a6a97e74b614cac', '69777260e05ab12ee8480c23cd2e6acc6e218a12', 'e86f70ed20b2d2415f3f01497fd85a7f6c2ac2e8', 'f3b9afe62265d7559ef65f5aa692feecc5fa8f25', '82c6d429e70f1c782103953072dc6dbec650dd6e', '47db063c310cf47e4c544196acab2abfe62977b0', '2119e9f26ed988746ee22d139d1eb1aca2bbb950', '4d544d4f08b027ce318b088b112f03dccc566098', '5d81639907869ce7355c40d2bac176a655e52074', 'bcc712b45820da74b44209857ebbf7b9d59e0ed7', '11f3db0b731739017174e488670e529af4dc22ae', '40759595e2e4d3751da8787c62ffd659cde257aa', '75e37d7e2063b726aa0185f4b695f32cde69d95c', 'ca30c2d55b4f882fda34a13aff6f9ea510a959f6', 'aa42a11dfd99ee9ab24d2e9a7521ef1c97ae1ff4', 'c615c597aa24cc5a4552102f1b6d27fcf4e24d12', 'a9af5c79638cb6ac4e729cdd995facc6153e45db', 'a78f66a5ae7865418ee482c535e883598e1ec51f', 'cb93d764613d801a1185989f09ce2d6b76009020', '2264613c4e2f51f1332671db50b6af6284baa6fc', '15568fdbe753ea1c2e5fef571daf45e435999c07', '7804ea173bdbb0f401ad0135442c563fb52f895c', '7fe1c2b3d3a78ea3ffb9e269563653626861fbd2', '332e698ac9c74ce29317021b03a54512c92ac8b3', '7c97b239a5261cf030984f71427287196e657d95', '5d69a79948bce0fab4705c7d7d0f3b02f2548c0d', 'ac267dc5cec605f3981c9db7d889cabd59f09a61', '6d18d882b850a8d757c32bb124b1e42e60587c69', 'bce8125dcf5a87ed29bbe795cd7591d45bdf3d9a', '55aa7de62adf6a99e6d6268c832b61de6b37d416', '1d8ed875db6ec624cdb8bc15ec677729a42fc13a', '99d650c2c882cc1f359a1dbb5fc6bea2d5b303b6', '5df535e8a349771942050f1e3fd58851f413fa3a', '7c5d0c459f1d4a8796e3b5af925f40fd4f8a69fa', 'f26fbb9adcd66a31740da5c99f14a108bbc24304', '97ad549d56d3432b0c70e72d402ebec1d07f8273', '6cfcc9d553622e7d511a849935e9b504f947399d', '604b93cfdee7a8c5c879ef2217d50be88e39ac89', '2a4ba75e1338eaa97da87077330b43d7448a18bc', '925d6a2ef363ee540e65f7ec3b699dce05aa591c', 'b1aa1912f08765d9914e3d036deee6b71ea009dd', 'c28c556e9295b9d499f7b1b0ec65bbb4d1ad4b67', '0bb8e66184931e2f7830cb713d9260cc0f3383a9', '95ad03540f0d15ae47fd73bae935ab1cb3e8f4b9', 'a6fad27372f9a931540c2e4345e428b643535d46', '394aa7426117d0d04666c1c2a63d5f98229b7894', '2afac715666347a30ac54cef1485db369288b478', '9eb32eaad5fa3863d60b84ef095fa041e83b7b47', '964693e40d9303d5d754010f03731d2134bae749', '28f1fc2f55269f43f0b2bb769b78f80b8cc9cf51', 'fc0d0021cc74bf957b3ebf7e5d98e40f718fe1c4', '2e5d4af83f6b9f87fc84c0ff10462a5414ff8cb4', 'b8f8ce4146af4fbc7ae40380f1ff251b05bd8773', 'ac542c9a835d3a2dd46f37712c6b5438f56747c0', '0c0d8363e5787e97cce5e0b9b86486d737a6890c', 'ac9c3ed0b43ee848e6e555a01c55ea2eee78540a', 'cae42215b7597cc39afc682cb0399782cf42fe23', 'd9e804b88952160630eb305ff05978b66cbbf49f', 'ed51b2cdf5bdac210a6904bead1a2ca6e8411406', 'a05e33693b66ac38ccb21f2238c194ca59fcb6ec', 'fc6e92260c3ff3b5ef72ac05e7a518a8cb2e7090', '8725e3604b31d0bf822959fc6d870512411c7a05', '31ce3a27050443276f8a634443ade5c251766614', '1b170df31c7304f6d4c938b5e0c2a09ae1e9189d', '04a70bd3fe40628462f63955c8522cae625feee3', '2d8049b713f2b9982bd43e7340a0e0f302f30d6b', 'f448e49fbe8c3c609f9beffa2f421fc0b875aa3d', '564311f5cdedd63fa03052368c321db5ce15dc4f', '2b5a59406622affa1e333e073546f075f59b4ac9', '7afcb3a64c53831f05f99037dfa07aba1d396c48', 'fa1702fece04c5fc50149fc9b05d77a459b7180b', 'caca1fdc90b6ef877385b044b2a3230f8e6e9874', '1e207b2ef83db4296fb944ade4c301794f9e8460', '1a324d784cfc53288730b7c1b5c1bde0685e4686', '58a910f0a7e6e2da4af9f1fcebdabeb22d909fcb', '133108cdacf7ee1cc4569e797f2cdf9ec60f7fdd', '0f86e6884070ec9c867c9354ae331aceeb90b1e8', 'c470ff70d3e829c8b12f6eb6cc812c4162071a1f', '8bf20bb58637a59aa816e9d9668741a15fe9b55d', 'f24a62d4acfc48e23dd4bbb854668a727e9445a8', '1facab387f8c2e513c8b7397430251dc44970e35', 'f33e9a346e22e29bb66e0ea0f3442903d136ca67', '3683d475edb7dbb4990ba6da7861552a602537bd', 'd166485be15d95192ec1b2818f815fa58726ec54', '837f31dd1850b179918f83338b4b4487486b2c58', '0cacbcf30e0a90ab9fad7bc310e5477cf959f1fd', '718d6f2475d377fd220da9898d25f7c8191f98cd', 'f54acdbb13a6cf2a7ef9979f5a4e04240b1724c5', '74bdafe386ae6080d6f5ead852f9026061a65be4', 'ee5cc974d2ef449444861d82e1793668184ca86f', 'd98ec33364d4ddb6ee53e0cd6001857207ba2089', '4580d9f16aabc44f3d97931c6bfa29de1e40bf2d', '53ca52f855e903907378188d29224b3f9cefa6cb', '301474f0baff19400db9206c502a9f1e488a5374', '443091b844d4119637d252a5303568e22d4f1d48', '339fc2379dd0d01bc039518f70b017b7520dcc9f', 'f2cb8523d1d9c74ebf2fd6f58f51e8afc95f01ca', '1354371d4f4dd208458c776e1f27715ec5f77f47', '347b37e31945b54fb96fc7dacc2b2deed40e8e5e', '6732593bbad3d1e07f1ec7c2d00a2e1eabf382f2', '418a91b5d4e3a0579b91d286f61c2b63c5b4a9b6', '954be11bf2d3dc9fa11a69830d2ef5ff580ff533', 'b2ed7d80dea092e4258d04c191d42682a4db950f', 'be0f10e1902961daa51b543f293f7038dc6e4344', '919409bb2331fbd60cd3d425fe7d9384d66967fa', 'd44d5d7520d31f33feb03b0679f8c552254dac4e', '57320981bb98086a0b9f836a29df248b1c0378c3', '64fff8bfcc9b92769640bfaa692e19d0db8861a6', 'e38e3a66ab099996ecb156ec9105869f3d9b9228', '8c47a3e52f3d6871a708b1d2c8b5a7e30a2a5b99', '2f6240ac944f2a55ece50a179b79f4e2ee63a621', '82f124ae30116b2b7d45878aaeb9699b6bdf1c30', 'ee0c2cee214b3d46f83562467bb1e4f5165f172c', 'd11f6f26b773343246c217f498de3c02916bec6c', 'c83ecf4c55a2dbbb89c71080c601417adb8b699c', '487b95454233e981a65c184c7f3b86adf34058ef', '7fc7136ffd28245629f00870fc7f7cbe1fe1c6ce', 'a932520135d42c7d9731064d96c21ab2fc5de696', 'c8c3a7dc48dc1ec6220798294da123bb617a6cd7', '08a4cda34e7535ead76d2a65826ff9e09b8bdd4f', 'c3e7c0bcb258061568294c0d96b62fea94ef8ee7', 'c19213ab4139aa1f56f89982448184a5c82f98a4', 'afe00effe2d79e89e159972cf1f5f7ffeb0c6e97', '09fe51f3eb7e3ddee54cfb210d8a22327d1b0773', '3637b2c374d48e99e0a7be37605d3c79f2661007', 'fc5f6b0e46ff81302b3e445fed0cdf454c942792', 'cf2c5e9ffc066aad37090fb6f2953a602cd8621b', 'b9f11796944056a5b3c7440033d587d19290b3c3', '811c6433bdd6c888860d18e28bd9751d57ee8e0d', 'b3771ba22acad7870e38ff7f58820c5b50946787', '3e64e5b9c03f13f58e6a049ff1fed039c6a15d69', '3c14feb442219f58e0008a6242a24f1a492ca337', '0d778987ee3b920b5c876696385c0792671adef8', '80294d0f5a7a224c478578253782644f4b5ad724', '6f48978b629d7f6eaf3f82783789cf46c3566d9b', '05ba5aa00847b18b74369a821e972bbba9f155eb', 'b8651d4e82d45463d2c71798bd5852f8a605b440', '25b13bca7b696b349bae2e9d5e41b75496bfdb1b', 'e2ec2d79c8d5adefc0c764583cec47144dbc5705', '283a19481d1ce4a77f5f465e7b96288db22a8ff1', 'e24d82e0eb66b62b5d74f2c60b1eb5db9f378673', '607dea234f30a0c40d5a48f518a51372e02fa0a0', '7bc1019d4bdf2b9592a7fc3e08659aae7961046b', 'f1f719211e5f28fe5163e65dba899b1da796a8e0', '96132e2dbb69a0c6c11cb183bb05cefef4e30557', 'e7b6d65713857e55453e4558957fbd3c7a68ea04', 'd568f73fc6ece3d29989413174eee0195d3d0a4a', 'dff1a3799c1c25b1b0009be6835f2c9c78cb5900', '2a0abe55f0b13ef0e9d136551e29a8fae137c3b7', '044350f28e4d0edcc0503b2bc2593c0d25948ed0', 'a5335e7cbd1e53790b4bb5e95b6f9027e17fbf2b', 'ca641b3e2e48c14ff308181c775775408f5f35f7', 'd76805f0fd2d65e9fb5582e4e0d30c1bd3fccd89', '1bc620d1af50d24b4d5f12147e425bb7cb7f19bf', '79757c90df8ee8390aa256998ce5cd1394d8822a', 'ed3bd79633ae227ad995dafc3d9f384a5534d4e9', '87bf5c36504d5f5353d98994d5c31d033bea00b8', '6d16879c0ffc4b52a544a8664329d09101832964', '578205cadd0bf64d671c6c162229c4975081a9d6', 'f393b2f7dd477c3a43e70631f7036a211bf5d740', '21f88a595b5785d8b9b9a17a3fb667b1712f3b61', 'a947fe8da8646058335ff76b9a744462a3337a63', 'e9bbfde10f1d73e500063817ada7a9ad474bca3f', 'b403a09ec0d78a352411414aaac0663885f777c0', '29a2921a3a93800f7cc1fcf97118e512961f8beb', 'e8a3bc74254a8e4e4aaca41395177fa4a98b480c', 'd71cb54672e63d2f0f4b999668336642a9a63a1d', '12f310d50e7f5b1c18c4f61a119a6cd830da3bc0', 'd2f37944b6043ff443adbdf547d5bd335affbc1a', 'c3e7d6252c41d48e74379810595c978efada9efb', '9646602d6832ad0a5f2e9b65af5df1a80a571691', 'de24d4a459ccfac623e71722f8df8b1e99f2ad43', '2c305dc64c033ffcfd6e45548752f0702f373032', '7283711c0d99d484b10dd7c460f7df01a319fc2b', '046b0087bd76637bbfd813ccef31693fa358ff2d', '975b680bcd74661eeecf21052f6185cd637bbeda', 'dffce6e7ae047a21fb40ec2c27d3d1f562a511a3', '152292994e45d5bedda0673c142f9406e70d5d3e', 'd413117769efea0d5212ce29445049918209fd03', 'd6698308194625e7921b9c3ace27a918f42f26f1', '9d544212e69269f155bb3a51f94a9b13cf1fa565', '8e4799b017c6a3c65eaf47f60ab136654c9741de', 'e32567699451e2fb0ccd63dd8a3df582cfd18d61', 'af634a4a98eaa2457752e3f2841720020e0e9ad0', '3dffd5df2f7d4a8a3e512be5af908328c9d5e92e', '2c44e77e2f2056ee88b4b9cc8bac1f769748830a', '6cae2577bad5895a922dee96fdcd69182ebb7d0c', 'aca027e96cff818177e52196f676a9d3a2a980b4', '9dc76f8872b862ca008562cdcf8cf50524e2eaa3', 'c6528424dfce22f18a6938f1bcc9409811719dc3', 'b02b5b63a5d43ab552c6cec1237707b2edd1bb36', '1aae604861068bb7337d4972c9dcc0c0a99c374d', '795f7331e27602fb2579d2aa12f048d546fc4de6', 'b53f84402890636e4990b102ba9f832a98b4e5df', '0846f590dac5d3893d41e262d67ee8a228dcc332', 'c9cf6e2176bec3091c069031c70e46b0162f8095', '201da0542726472d954080d54bc585b111aaf86f', 'eb8cc09625c9913c4029763a46d4b7e5639c85f5', '1f9aa01a5b3b59d90499a059d719af03483d5130', '12e8b8c2c7ddce09daa449d9843e9fa08cd538f1', 'dc91ce58af941b822fbad3b560c35774130848f0', '460e46c3bb76a361d0706b263c03696005e12566', 'f716d08c9c7f0f3ec7456cb9a27a42104457b867', 'a42f16f980cba86a8889be8b7499437ecbc2cd42', '2aca6241624f6b924b3e0164a9b7d021d80096b6', 'fd79dd060812af0531428cb2f7fd4aa74ea8124a', 'a648ef0b44e9c3efbee3d66fc7dae85fea24a74b', '8406f3624d8f99b614eb7171b71fae8b0e663dcb', '6d649f503a964ed9612e79ef2d9e55e26240fbc3', 'fa57eb065d032c225f63a0b2ca3f050e728c2235', '213466b3d4fd21b332c0b6882fea36cf1affef1c', '9bd0d6aa93b901be97adb53f290b262c7cf1f175', '02221bd907a23041c95ce6446986bff631652b3a', '7bd81628d9f6c21649e615e73ed507520fd74fd9', '05668e98f531cf4d6ddb0696f0f72675ca128581', '3e8a10e7d9a9d5d3d29c8793e30d8401be1588ac', '075b368d47ad3144518e4d425f9155e35f15f418', 'e56ebbffca57741d398283e46073ed4170f8f927', '409fabc5610443f36574bdea2e2994b6c20e2829', '172fbb2dd55144e2e44777174f970b56768e1777', '58d6043d4c88892f6718e31799503c3482000ea2', 'a552a2704e36b6db0a6295370d4e190ff212d5d1', 'f6c59df07ed865a8bdc777db73216347f4882114', '0c2d829249567c6a87140ae25f631a2abad11d00', 'eecb403bae80b82b6fb7a27bebb5b53d6c8f3115', '0f995ba6be1c0f949320b0879241d9a7c9578436', 'b4532a65f758448c763b65b2fdde1405db2f9d9d', '56c64803fa190814232c5f71e9a758346c474bb4', '191f2faae07b6608b0601dc2caf204b196a8fc47', '6ca899e56d451eef04e81b0f4d88bdb10f3cf4b3', 'a4f7675db1a928e73c7a69eb906dd1e9ecd4a22a', 'b0e82a68b34512d23a5a60a9b5da0db86eb880b2', 'e6cff21fd8c5add0eb7e55417a91f0530a7d3a32', 'a97e55b83868ff786e740db55e73116f85456dcb', '437382cceb8549f33948398c92040e6fc16e0ed7', '2208f9966d31a3f52bf9b63938bd6f3729378d9b', 'e2847e8603fe19a87ff03584fb38954e4bd3a59e', '9fa331b811d893a0d580e9136c1c7e1fa9774542', '11a8a8d274a16ee2aa8271efc1f7418499372e31', '5a30a806ec1502cab34555bfa4df4fc6ed7a7917', '012880d4246fedb5c1ea7621e86217c57fd217e2', 'cafdc1e7df7725c92a374ba9528f7974fe592196', '4574b2438a82115c5ec3ca3c381270b06fc2a8e3', 'a1808962e56eaaa3657298baad3fc09abcc49040', 'e6057e5038c2b681152d2608d551db675960d12a', 'e82e2e723a8a71e5e0a73af4ce408e60065c1356', 'fd9a20e4167c51c6645c55ed98800b768518c863', '78c55c8d66f5570d975caa53a9751b126ca10538', '17712b96c86d6c2cce695531d7e5f2e3d5718b21', '055c9c7bd6780436d201f5101df464814ba940e2', '264d1d8a8b7ede0fb8e3595d0153893966bb73b8', 'cca05cace4105c829f303c13eed8ace2efd7fa0c', 'bebcbe3a049f78c4184404f2dfb8b4150233856e', '8dd4fb5ebeccbb427ec5d0d92d4d5281ba9291f6', '1b9e1073885951697f34950a1ea706c93826e871', '47f908fa73fb7bbaec553635e75bffddd7a473f9', '1ae92914e28919a97520e91bfd0e588d55eb1774', 'c7db82b59a145735fce227436c6191d1825fe384', '03d61ebfb93aab53b0b0ecdfc77175d18a58e861', 'e5c4ebdd7433d746a46d6962ee04ff2d782d892b', '23beeea34bf9e5e7a33dc0566d54a17b2b417c0f', '53b4ce5c00d61be87962f603682873665155cec4', '528e5c0f57aa5014cd13baef2b683a1a328f459a', '3e48bdbc333602a042b6b0fb7fb9e14625ab4ece', 'd227f940489434c2f23491340d4399d98fd48d2d', '53b10a37a42769b0c1b91c3a66fbe7f60917cab5', '62fbc92e3d9d84617dab46329c279835d157bda4', '2a133ba656ae550cc9ad4bcda0a9afb27b14f4e0', 'a8aedc85ebfadcf1472acafe2e0311a73d3040be', '69d86a197f86ad4c6f1636b5ab4678907e30a4c0', '6fbac3cfa88bd3eba36b2d3e254d7ce5e11a550b', '717c55be971df862c55f55d245b9997f1d6f998c', '1546fc30af520f8d133aa3454cdc8a036bae0f3e', '320f7b0d7afd75c6d76ee33770461e93bef43689', 'e867a58425876767b952e06892c72b5e13066acc', '88087e91dd4eed1ee3c3e12961db84b7b56c34a0', '39158bd944f7c64e32634cec8171515b93466ffa', '35bc53c55ff317890d13490443a8685adfc33084', 'c6664e1d081155f6a24ac940fa07c2a061e71cff', '6b24b2e8363180cc9cfbca11dbce9cf60c7013be', 'd2741e5cbf546117d9eb3f6776f9063bfc240d9d', 'ad3ccf8f317bd28b6527fd4d5089ce8f0a37479f', '0f177ea6b826cd96f7a4a687da3691fb786a0a89', '6a3a58039943aa047fbd07abfc7eff6431c367a5', '83c7c0158fc62c294267f0ae60e9f52945e6a678', 'ac385c4c3a770728848438f28f4acb8854ffc868', 'bab3e082dc48bc3db68692694bd114a39b41fa68', '4962c8193e2faa589914be52962c701aba0980d1', '73c6bd8ac9a63de83162370a20f5364581fc9576', 'd91a94e7bff7a2ffe94b562ef1108307afce1bc7', 'bcc023773ff0f5b68f0e808bc7c7aba457a4c472', 'ad3b8437fd9ecc18fcfa8204e43be9bccb1a6548', '74f8ae13034d90800da5573666dea3bea5d702ce', '22434e6535c6471b8ac3e9cff1919e5ac15a50be', '9fbb0de5fc493f9186d23ccdf03b772ccc01e1d4', 'f6f24c470004063d14fa33d8680d1eff75c4f598', '3ca204b427971af161fa3550c46d9ec796e8b401', '6925775e63fd33e6a44bbda671b2de7db41d150e', 'f98013cc501ab30c43ca707a1fbf46c4e9dc1215', '52007e878aaac3f48d0d949dbe428a2ae5e56f57', '7349608009df3d231db154b9f5c15134b047e38d', 'd88f1048da6bdc5670102bbbde7bd92fa7af4ccb', 'e3c2148b207a6ca98c89211d12cb47abdfaa70b3', 'fb9b0dfde5f674e2aa9f9948e2f14bc7698413ae', 'c55277cd79f103d9c686b9698f9f63208fdee272', 'b24e9f3ccb68dd519aa3e6d3a1b3745c493f201c', '3e24495c69175002635fbf4a2ebc249cc920e4af', 'ed078bd702e30b300050aefd7a65561fbd75049f', '2cf7b35d90f56e8f5326576d0683128421a37a09', '2298c7f84fd0e29b304eb11078bfe76ca5b04a97', '99a4068c5ca61951d70b9e647ead3b08a2af4309', '6d8da96ba8c892aab53ec9e1d5a074c5043c2092', '5987c76153085cdf729a72f22bfce9e2f815234f', '27ba8f5a399cdd799a7eade52c31e6bc21ad18d1', '465f810730b62e1739d0567da1254ac0167248bb', '9e81ec9a6ebe2913687f66cfb286177dd853ac13', 'd61b0c131ced14cd3a006ef4ccff39f6d9ad51e8', 'a41bc7274b8902036ff75e0b25181d6ed80acd5d', '4283fd640cbad7af9d4d5da25b6a7d4edb985a1e', '6df058ec150b9c3c8938740ce5ce95d256f4b012', '4deb80676e66311dc919f97dad1cac6131267885', '38ed69241f928530b13a31edfe918c0e695e2e9f', '19681f6b95a5076af7020475f46d0bbe9f6015e1', '3daddab8a89486c519bf6ce22f8765af8ff6d356', '0b8ea7e162e68662fbda6893d188862999d9b427', '38fde81132e0457e983eae60b3d4a9834ad84129', '18c13d38ed7090801f125088e3eaec7e3c85c09d', '5b6de9f2d022cf25df73067b9de77ec267b4af8c', 'ba365bbb8deb16ec133e0e7983c1c56cef0152aa', 'a87decb9e4df5bfa092c26ae4346f65c426f1321', '7e36865541307554ebad410d61c8186f6d641c55', 'b56b848682079052e98ecde9a15a8f6b8860c599', '2ac78ff621b1755b84daa8196197b774865357e7', 'e9cde112cd47d88c3e256881a70035c0fd31c022', 'e94f5dcc802f8e786e84a58754c800c0fbad5ab4', '505e8240cd29fe3f0effa96d794bc711d5c8423b', '39278d3089dfab4377051ed1ff47edf75dbf8583', '6da70f9b95d2f7cc1f32de4e405661a8015bfa7e', '81fcb809249e9f7152232ec8aa66a79155327e6f', 'ed7c071e07f1f90e4c2895582f61ca090ced3c42', '71c5729a41330702ff0592776e2fe7f67e472b2c', '0c76fbbbedb5d342080dcaf0207602c158d3116d', '6d87c12729ac6dc9d39949c931fad4c45c6af841', 'bbd3348dcbcb01017914b64dbe6f16c00c1b7b61', '8da12d4d37a26f577726097f284308c32ad07f9f', 'de90175fc24357e20306c5a4de4f0f8ec8675ad2', 'a565d1a182cc69c9994c4512b5e9877e97f06cdf', '57b2f6807d332f4e6de4dc4ad18aa24686d304de', 'bd5a1c4d368b9e91398e48400965d30d3045062e', '05b2361c0412be25877cb106e9847e9a8a4f622a', '6b86af3a2703dfbb9685b4440ba7538091ca3e73', '4090f9a2b0a95e4da127e4786007542276d97520', 'ae0c6e58e5a2c545ba54eea5fb4d5dd103d237ff', '5380938f8bb0cc2dcaab187e381f369361dcbf79', '059a81e3c95fd89a3cb444aa9ffd3578648b4549', '0f8698a3292333ca527248d5cb08bf1a3f6b5def', 'd76fa1b22d4b06e1f1a7700272ca963091f13931', '50aead64b962139103e0d68b446156e9c0588cfc', '998f1520f9af2dddfec9a9ac072f8dcf9d9004fd', '0a5dec0b3a3e1b027230ba766fee0c08b70cc63c', 'aa0d0ec32ac53099ddf33e04be2a1701e539dffa', '07db26c20fcbccbf758591607864f7fd4bd8975f', 'ea72edebf2d484e42a4251c53cd6e383743b5d1a', '7e1c39f75abc443281f31f3c2f1e4829e738619d', '8d0b069b0fbda0826ad447accbf93a19f6e813fe', 'd8756e81c57040130ec163e63798663d62298589', '937f1f741c026767b6b71ce0b0e63ae7ebe6e936', '863b534f83aad50528828a30857e1ff56ac93f27', '0d16c26e3c0affadfada1ce91faa617dc065dbcd', '3b6573c150527efb3e90b2b3d1a059ac5c2db80b', '84c4025fdd13fb6943568ebaf78d7ea2faa35540', '97d107a270d3e9b795b42d209f838f2f81009ce8', '1de9981fe38f3e46dcc98cd8b5d3378f7ce0f6a8', 'a28447a06582bf8e6de427f047f3d3ba4e2219a1', 'dfef2a3ec2f69a5159cc6d1def3130197dba9cf9', 'ede07306a70689f51fbca4f13d3b65a2326bfa43', 'e0551acce434985acbbf28e441aa2d4af033b1b5', 'a1f2ece323e59a751a55386bf06beb5724c56545', 'c2ec974e81f1c65aa2f2e43f7f4dc820b1957bca', '78374a15492cfb6df49353bd166d8c45ac9abdb2', 'd3c067f35d184ca75e8cc59bedd56689cbc8269b', '27755723c23d475d915befe58f4c63966e3b02d1', 'fe0b704285ebc916ce5080a5248d91b4dc3c60e0', '33f6e1a1cd4c38728112ca697bb04293d65cd968', 'b0fd308b2bb8b199811b1d37e4582c2898093e54', '71113b4aac43a3a6f13625e25171ef8f2233fe7d', 'd3c7ef251a1ff9dfa94cd462d736a9f6592106dd', '2d2311d75c081745c7a24f7da7c8ec14feddd259', '542853d99b9e0d660a9cf9317be8a3f8fce4c765', '605d55dc173156471fb05c40d715ab318ce74952', '85c96f523090cce5fd8767c0fd3505ee4885f63d', '9ee8277cdd4bc02257f5ff6cffd67d823485562d', '89cd2f2a0b21368297323437fd15ba6341e4707b', '960b2162903dd7d07f20c162aa536b1e67b6477e', 'c249ea8e5d9dad5e74b804a6f062b059517d6a8d', '635b5fd47c86809401dd8f3c0e9a225526f96555', '6cf27c9c771900baf43cc47f9b010dbf7a86fa22', '1ad5f346cb88072c419142c86c3d897aad2de996', '1b36e11967e7a459d6401ba5ea740a659fc1994b', '8012f47e2cb955ddecc3bd58240a84d021ed571c', 'e1cbefef600ea7186ffb92cf2f13c4c87b7769b3', '9d9b59fccc1d3a71758dfb7dc6d2325d4fba9955', '68ac551966e2be5b13adb2f703a01211e6f7a34b', '6ad540735037bcc668dd94c65d4203d0e2f63dba', '353aa6206d43b4923d21510797709bbbb210f6b6', '9eeffdfb7d47a2f25b9b08fc9a2d25b8d1589220', 'cc59150f408de6ffea277267ec7d2e3920fbe6b5', '2322bc0a6fdf8b26619b6aa9f09355d6c25e6298', '6ddbf4d800403de33e08273e92fb0e7159bd9f22', '7f640f13b4d486477035c3edb28466734f053beb', 'ab5e4f52ecb072df55c7f5cd8941122a135cdf79', '0eab92012bdba3f4b8b3afafc41fbea751179671', '82dff1db5486647b27e5297e4c839bd2a905c1d9', '588e8a5be83037225716a78358d0203a9e070168', '29e08fd2c2d3349ba5cbd8fe5a9d35a0cea02b85', '94ef25ab9128030de8692b6e690787ec8012830a', '98ea773a22f84eb21c318fa1da78d2e6b323e8de', '0fad944ca777e788e21ff577a1f1ab3b5a7f1266', 'e131a9963484e46f45a7302267507ed54dab6871', '6b6afce60fc481db104a09c51b4f6935c16662cc', 'e032a360708cec2284f714e4cae388066064d61c', '37f20c6924bc2b2d861508cdb79b78152cc01b60', '799183b3f8d5e8d4c955e7d3ce654b97f8309aad', '17f98792617a6de39cbd0d651a03fc40a06e0ff6', '640be46ef5555d6e957589ab8cb67cc31fb5a4b9', '5c0fd36ee53052fa8a92dac1266fee3aaf16d7a1', '5bb3415c77cc61b7d1591ccfe028d10bbf9f2a7a', 'f7edf46a5e5c30310542c00eb2d771bd72f42fd3', 'b5de44937768304ba17351e7e742f4258533a150', '7907b5a6e921cec3b5fad2a4f84a78a851140755', '95edaba8615d4ca2d623722eee38eb78fc24d317', 'fd095aaafdd207162cddf293b17f249875b9a532', '945356724147549421727eb52a4ac33efa920b2e', '1daa8f638bfc8034d8499525d569660a050194f8', '71397adaff8fb4e9dd126c51cd38110cbd813936', 'fe2742e1b23158ca697cdf00cd027031403f979d', '318063a16ab8b34dcb036d995bf78b941975e8e8', '58f7a973149f66b980f99c3f2bd688d70b06b2eb', '53875bb043c9062078887453a5c09216ae12e89c', 'f50bd5f80e33598e7fd70290f0c0e809443bc9a0', 'b558287c0b6cfb8ac11548048ae12ca6b5c63257', '27824baa210b9744074ac1fcdabbf1c8ff1b42ca', '99115cba00ae35f19ae4ba8bbd77b471d7e33ac4', '075bbcfec40a46c58ba38b58d8a9235e2eb1395d', '2cff1fab3f1c20f464f75f32b6138707050199b5', 'cec6202369315d183c410ff7145ae414678b0763', '1672a75e1f04ce3b7cd4fa202942a8887cf811e1', '83c616f70694637627edb6b1215738c78b74a50d', '9c833f665f349e5c292228f75188f5521282835d', '36ca33f9cf1902399afe7494f88c6bbd16022b56', '9d52deca247d9e16cf530d655891b2bbe474ffae', '07360680138d8cd65fb50757df0e4bf9b3ff6cc3', 'd4c1ef4a100857659ff4ff055bb743dc0471560e', '9774c73e43dc2f628b9fb07a30ab4a595c0ee0a8', '910f3179960067135ec8ad4ab83d4582ff3847b5', '4eb3f75556b31b866384a58f4300294624d71bc1', 'd4ef46c69dbc525f55a6d9ae5c9ae6da795f98fb', '5bcd7cca2263b4c7909c46a9fce2bd8aec2c3835', '8e083e1b8879e732522bb9a560c82ff84b176cc9', '42f46abb34a2b29993b1a8e6333a400a00227e30', '03ed08d0d17a10ca4f96c8cc0a8694834ae01e6d', 'bea5ceb4343e79ede4107c879288a5f690bd455f', '2239004907b29e00811fee9ded5a790172701a03', '147ffb9a968d62ef78ac6b330a20023ed49ddbb8', '446196d6e9d66f45c64b483e3d375aaeaca28e3b', 'd8cc6fe8e36329c647736773d9d66de89c447409', 'f3c00ae1c86a48ad36fa42e8e7e1d2b448790e99', '965877c11c221640b6fda3cdb55e78da7551e525', '712cc5d073da7595dab836c95372c13fc61047ee', 'f7c38d83c727310f4b0678886ba410ae2fae9379', '448e7d7f0f46db1eae14d4fe7a1d25b7af894b09', '3348fc49d8824155e737b866f633e14684da5fe9', '30df089631cf3c7667b609e2b9689cb02230c64e', '55b5b248cd0974f48e8bf261f61227722cb47570', '6f3ceea5c61d90d4075ed5c5a22bde33142f7ec7', 'cec5f377b59443a52ed1895d4e76e6f31cd46d66', 'ffdec02e25bb3be52ee5c06fe76d388303f6ea43', '9c8f75c4b624084c954b4da69f092211a9ac4689', '9b30276e58a26d68b6665e71dd1295abc67ddcd8', '194c56fce2e1b35b518f54ff0d2e5a1104a8126a', 'f748d59d28bf5882286acef896af7805a761b760', '783a6451f5f8c50dbe151caf5e76b74917690364', '4cd2396db31418c90005c998d9107ad40df055b2', 'b351493543b7e26aa345df3b568d0dc08a8c8d91', '990301323cc5a774e17b0a5d373aff8439cda489', '92828d53b18703000159f4dd7dc8b3170667db25', 'c8bccdd9137c285c1fb550a961d37654470f0fda', '7915d5e5f826a71c11e1c9183c84702410f7209a', '5645d906fa319206a9270c080926a21dfddc852a', '1a9697430a8a6da575fdc7e96c1aa9a5640295df', '22bcd9fac3c988c15862d0b6c01930814b676253', '9c8501f5f0d4c1f8f4928ea98c889954f0e06cf8', '09d1498e3065eccc26a2d396ea13e7c8b553b974', '5ee3ecb1e3bb4f6a8f8e344229c1a88b1d230e73', '7e7bb184d2abe34280a7f0eb0f0d9fc0e32389f2', 'df8918b90f558486029985a78b545c1e8ee0f6e4', '634c9175054cc69d10b6da22ea1e95edff6a4747', '9fda3d206fb6b4ff58792f6dcebd9275e46427f7', '59c1d4ece309311dc670c280eea37953049ed19d', '785de1e3d459b26c3ce383986827719388be7c6f', 'bd462df20321ff5b75a7e3eae70634268582d90b', '340cd07bab7ea0f9caeb0cbf50987bbe38cf126f', 'd1c82a9baf5f3a0d3bde3ec35157358f7a62ecc8', 'a15387f34d142684859c2a57fcbef25edcdce25a', 'b8090add335903e6daf38ea99d0f94be5a5e6ed4', '69790a1463bafc1f63e3c288a6636b3f4586c1b4', 'e0f215bbd3d1da21e97ad03c4375f4e7225b1a82', 'a8b5f5a430412aa6536beb2a93237a1b768693c5', '65c29a9dec3c8b14775344c2edf5c929ec5d1694', 'cc54000e722f2b258ad6e04643189327bebc5b77', '4ab72780dd95c788a3b4fc22906543ae7cf1948d', '1f8ac6e6fee3aa404fc1a4c01ac2e0c48429a306', '7e9dec707dab344ceabf36346d1611bae1f20a8e', 'e8ad80ebe152a2ae55272b538282e407acab5cc4', 'aef6cd6e9995b42db2cefa7d715321d0edee5628', 'b3e77dfad93b9e4d2798ecf84e777c99905d380f', 'c3d0140a5577f85ee64f739f5f8832f54612de22', 'ece01e6e83c174ee098d355052078e9bbf536267', 'cfc813537e955c267106eea989f6aec4879e14d7', 'cb3dc6785bc8263d8e1fc695e499e1eeb9df52a4', 'e7090173eeb72ba5c125ded2ec1cbf63cb5c0674', '5899f5fc4a9557c33e466e58d7555990ee742a2b', 'b3264b79961b9bf32cc8f967054d634c4c63fde6', 'd12fbb7c0077fba23282adb84ff87635d6e44e5e', '192db666c913c3fb2bfe3568ac51aa4ddb010a25', '351a22a12ecb79a1e1070bf1129f2e168e4e0670', '72918a98161ba71ff4fa8116fdf8ed02b09a0580', '2a409200dc9d1fa3198908e231161988affa1754', 'dbf4e63ae7e4c637917124c148d5f21193d395b1', 'dc55be2d92922dbee28d010bec464fcf0e90a350', 'e63f591ec4d4664608286072c31127323371cb96', '4cc6d204ec5cb21841a2f8f21e6333bb688cd892', '9ece7fe372230e848920d843af2d0d5c2fbaac4d', '310e240abd6acb6ad27dea8b5aae01c1a7674ca4', 'd5dc7dfe00b3a9c9934ba6627b892c2c76732e1b', 'ea8ae976504e7a3f5c6f4a7efa5069d03316b093', '782175dfbc4a6694ae9a3970cd92ebd20647bc7e', '5b06e9690b0fba37ac369149f573b27abc39a721', '2e0c686ec1e1b0ec30b0fd6fd53ed599fe718f8c', '393139064f79ee664b6d8507ea1f974dad84c82e', '22e8e71156762b40ac93e2cbd42eacba00dbfb0c', 'aedb53a4e6313398ed9626fbd2fbd7106e2ce94e', '94a0aac81d2d021826b52db1285c6b13b5ee2838', 'fec6831b1063b7d9205faa79d4cd725bc59d427c', 'bfc96efa3a9a004f963cd4913fb2875c84bc9cb8', '6752f2825444ad652930d708c6665c5e49a9b69a', 'f7c0b0d1728c0d5ccac90776be86d2c4740633cc', '0158e1d7196748aa346da90f688048a32f75c2d1', '45550e98f031f7401b8ea8a8e16eeedc0ccbdc7b', '7b06a2417df0d098b8947327f77629f16d2f5e49', '647c609cef3914f7cf2923314c4e99714d86ca25', 'fdcf8fbf72bee9e672d0790cdbe5539846f7dc8e', '4d28391480fd8c547a63af119bba67fceb5d2ede', 'e3ff4b087050b5a4b3f819a9535fb8275a380e23', 'bd6c212538984399e78ab6a3493a15270b38eca9', 'f5a2fbac6d26fc1613ec34d9764a1e86732f0a45', '706f5097b70ce140e6a0473ad55342b514b6db04', '5552da3a20ca432626d62b0e892bd25501484689', '470e6f2b9ac45fc32bdeb77d0bf4c72d26c6f3fd', '0bc394cc1d19f1e41dd6646e9ac0e09b91fb1398', 'd2cb2dc45d536ae124e3da25d6d5a4e932a23a27', 'd4de383f5fe8e0d35bdb82f85d71aaaea77d8e90', '744d05b12897267803f46549e8bca3d31d57be4c', 'b35ca4349dabb199411cb6bc4c80ef89f19d9328', '954c090ffc378a63ce3c3c9a72b87724fcd2cd6c', 'f75efb96fae99a22eb54b5d0ef4e23b28fe8cd2d', '958f90e710f0de164dce1dda5f45d75a8b8fb8d4', 'a92b986c855592d630fbabf49d1e9a160ad5b230', '23b61ba3837e92f1cde9cbbcbb172c545b1ae6da', '8205051e90e3ea16165f8dc1f5c81af744bb1b9a', '207bb0826be1be91c12ceb30f525f73aa9519463', '4fe4cdc4a61cbac8e070a8b5514403235194015b', '2387d191fcbcc6ae33fe5c48a933e6b14da7aaa5', '336d26accf1bbf7417299790b8860def19635a7b', '367eca083b44261d4e5fa8aa61b7990a8b35f8b0', '13b4ca87954160c73db787fd9f9d1af5e21edcb1', 'a290c8e1df371a5605d1eb2ed527f6021f2766b6', '80f373d34f7716a54fa9ec1e37a27c65cbbae0f2', 'e0cbd0fa664feddd412b23d75e12fa5862b24696', 'f91d592a270dc4254978fbdb51e9cd479426b931', 'b8058829bbc2eaaebba3ca959e9e601e112516ab', 'fb2eef94a4ae91501d869e2075c511b433877099', '482b79dc2586f186971bf7db8e5e23ec7a7566a5', '2342ec1d1cc03f4f244fc2921978534af63ae54f', '2f3600198722dd5a01a210bc78b7d43b33967c7f', '00ec646c5a1762bdce28ed9878cfa46961de37b6', '3ca5a3bb78ec2324391c026d3dd1b6773cf9c772', 'e9c5a3995abcf5ddda16ff4ae4ad19be97035e9a', 'c7ab9b0ef8fe335c9376669d6ec9efd90291c955', 'c9cab2beb8159ca83e6e2c930514ba97282e1ae9', '88eb24db40cc47786b6c72fa9f77032281f6e1b0', '89933f21f204abf75336464d3ac24a4fdd254628', '72236f343d644569c8699b2337b0b99b2c8d2752', 'c972ac929edaa32f30dae19714180cdd23addeeb', '3805f3706f8f3ebba81b80915c9259590525fb05', 'bf359f8e2924ee03b34a6f7e7eaf80bef55f9d98', '816f5c3b86ce94b1872f0d9d4bac9fdaa37b390f', '62221edaff412ca18cf32d81e82fd1f68752eaf6', '6a5992a814364a41ce7ad34aa6b27cc44d0eb78c', 'fae0784ce32a448a0d8c69e0b55f957290df1b5a', '6d1ae8bc007ffa1f3b3a9f949db6a99820dab75c', '4153e754f1a4ebbd9a3d10be8bf75a7057c82f1d', 'f2785f3c4f83bbc3caef48993018896bf73224df', 'b9acc492a568ba869a8abdee0fa965f5f58c4de6', 'cc6f4c49a9dc241b1b63ed7cf6ec982b05c541d4', '26b535226045cebba6a19174c50826a122778c52', 'b56d9e4416bba3bc54d2e167298c370cbbe7edbd', 'ae8b7845374b6bb01e1ae9ca27f6ab967561e011', 'e4f2f6f6e82cafbdea785d53521b96fe062e172d', 'f207bac20cf19e841d3ee8dbcba03b1c30d88836', 'cfe642edee80977173279f4a41e23aa822b9d19f', '48379bd6e5075fc73449e235f1b527114f5a430c', 'd4961038649b4f498b0fdd667ef4ad2a27f09605', '787b4fe9550389a2ca6d1131b32aff6e4f8610d1', '31bb4c368dd8723ac90a89c5b255506fba43c633', 'b38cadca230e85bc0748f975e6a8e8c2739c747e', 'c3475081c5e8228e9bd3a45022a93d61e542f72e', '574af88305273f21456a9b10f21c182181cfc600', 'bd72ef1bf1e40feb3bc17349a385b479fa5fa530', '6655a09e99125aff34cb96188924fca1df3387ad', '8cd04cb1a0e7e592d765a8b77dea85011a6e6f12', '4fd71a7ef1c79ec3f110ee2f9d38fa7d5194937e', 'a095e1fd8ca38099605a21c9c8498ceb37cd07cd', 'e00c0f3719ad0803620752159ef8315d668735d6', '2b8aff58b575c199ee8372e5689420c9d77357a5', '66e8b8b413fecff9106a5044870cb7c83c7b3212', 'bfdef11b9781c2cdd4cd526aeca3bf3898d34422', '5e5d5bff3bdaf807338ec9adeac982f8a5c98fbd', '3e3264760ba26e75153c655694c820a531935607', '02dc615c1e2139e1bc92bf2284fda2c740152a0e', 'c38adaf74ba1019272515a04d9cfb721183a0fca', '9baf5eb67b2a9086cb7c083a766d291ea0ba9fb7', 'd4877982bc19ebecb085e4009618f2bcf296eee2', '246a8afb63313907921407d92f9243cfcb4bce66', '4710a491463a91ec12c670ea50c139fc14134e80', '58402a6fe8ff0b395ef5d28008a58f15db8c270d', '5d52de2ccfaebc8d4f8d6ff247b86a2830eb1c8b', 'e5df7fd90b9e9a8ad7edd635ca9bb9d0511f2ffe', '6854eb8332d6ef1f1c6216d2f67d6e146b1ef60f', 'a594747d7566b28e3599c1940cb4c98ca9816dca', '01ab888dbd08ccd4b0de9333d21581ce24fe2c3b', '4835c64f89876db5af2bba8fa3ef17de5a0b44e3', 'a03bda61e068d72823af47e5f25ffd12c3319541', '7e573001484427dc73d821cc232a4c3bb3b5f5bb', '71f1cb7bfb2dc801c9ea47e0794d915e7f279635', 'e31a53a2cdee2ad8a1f71c9e8c9f1cab64393df9', 'b53412422496f6cc2caf2abe3139f3bc2ed75c9d', '807c5b4d282e7a7a16d0bb94aa2cda9566a7cc2d', '908a6d2ad7807642b917231769c8d3b6a0c1ad16', 'e8818e234a879d5ca45accba0121f43f45381f4a', '627cad1f7b6dd70caf88ec314daf1719ac377f90', '30d1989db139ece5e5c7ecec0c4177e008e14003', '67d21c494d4fd441f0c52eed4390a69eb605d9c6', '14c24490505090b0bedeabd891542915692501be', '965ec40c0a282bc311c702c6eb795689ec7ddfa5', '7acd12d6e3550d5e3768215e5bd64487c0a2ae7e', 'ddb6959c6272d2039ed8c9f595634c3c9573f85e', 'e20f102298cc3e056e079cbd1cb33913a9ade0ce', 'acd1f9c5631ed2aec2d6ab238e6b81c1a9eb47a2', '04787334a52f9ebde5007ba72162001aee73d271', 'c219fba421fde142729a42da038a8e747b71cca4', '2518c37f9a2c3bf478bbe69d571e22527824adfd', '7567cbc857eef4f535410a8f6256308197c3b9c8', '9078451e37a5e8bc8cd0c9865837fe5379e8f2d0', 'c182a431d224cb39c0bb43a55199e2d8b4aee1b7', '69645f1fe5103adb00de6fa43152e7df989f3aea', '529dde904a892ba2f95e6150066758e7476f15e3', 'df2236d73bc630a588adc049e7e63a4a5095da32', '7d9b9847f184ec72c4c80c046edc408789dcc066', '265d3199ec741928c1731397302f6c320cf6af40', '9d602a01beb0a9467c43b9ee0b80ff53fdd2dbb4', '000ba031c3d1135cb0e1fe6438d1e464ff10b6b0', 'fe72187cb15bf7dcc16e8630ed379e979c101151', 'af377b5f30e7cff691bb50f67c3e9a1cd040fe32', '14c6030c6ad70997863070f5b93c817ea8829425', '67cf187603f54ba4f53f0b9eb93b269430f829db', '319732dbeb08aa7926175cbaf4dfa8a05c52ac18', 'af6a9a17bffe654096d21190b3742e8835b2fccf', 'e6db0ca48c42c03655e3781b46ac09dae75cf33e', 'ca5d6f9229c170beb23809159113037f05a1120f', 'f1fdd31e8f0fd3145aeb86fb561e97e99672a623', '4b468a9d81f7380f7197a2a6b859c1b4dca84bd9', '368f4cee7afb269fab092cb58f6411f27a1dece4', '40b261f0820faa335fab880e1366f7e56a62eada', 'c8fbe26f3bd3a7c018e7619deba002ee454208b9', 'fdb98df839146a6ac742c4d603c6f73ae00ba484', '4c23e47bd5b04d49e1ef55b8a33ba18e72a818e2', 'cd4a1cb4101abcc7cdd1d2d0d73662114108f3e5', 'c2ee1411c8bb73fcf96c12abeedbfe6fde2c6354', '2ccc834faa3fffcd5d15d2463aeef3ee6f5cea13', '6eb57f6df1706c6ec228123cea1c120bd53b62d6', '7c280c1d5f6267acbbebd10d3e58ea4b8fe7a4ef', '0dac4b42678049e210759f38d897a75271aac72a', '539f006e65d9060cd46a4052d1b70f2312d8ca34', 'fc7133d52d1ad6d8a2c91ce2f3d550bb56de1fb1', '10f34ce6b3f6e43424cc6a1e1b334776b0171f98', '9c7d8577134559437c67cc2d24154023993a901f', '798275f25401b41af6a9703c117173b687d218ba', '84b41b9d3ad4b45a9137197a304996759e993cde', '4d5a28973f96492215531520b34737d529d8fd8b', '418b1221502aaab0c9d9f627172927b5ff2d1005', '9e8ace080990a6471ed058edffaafa74984649e4', 'b68463c250ec99d86ffacdbd45e58059d0ca51e1', 'fbe7fb941173c0907792a8b48e8e9122aabecbd8', '3a8f6b17a6a771d99322c78e8455bbbe4bdc6930', '0924fd3f9f75c446310ed1e97b44bbc3f33c6c31', 'c5bc2688da5902015e658667186611d5d060070c', '01533e3dd7388ca96b9fd3b84260a8df1b2a9d28', '46df7ee97ce696e8868a654cfdff483e6f180902', '1317f3f77a9e1e432e7a81de2dcb88365feeac43', 'ce7788aadaf989214c70e4df7ec286ed080e8cd1', '6b2a0bcf8c76e6387f0f5ec13d1a7f00341ba110', '3ceedac79e58a4a457d36d75c1f4c5a5b36af3d2', 'a2149c41786b6769df079e18d1f8078ef3c75832', '4ad439c56db551697ea366dc4986eee5fc288706', '4eeb7071076fc5028cc0e48410819dea95fb1ba8', 'c93b717836b6d92d0f2e9fb101ea6ff3e823ffca', 'cf5e714a8bea8bb1de75201d0769bb1c246b9334', 'adf5cc5371db6253e070b11dc78dedc99c8065f9', 'fa0324485b1e76388bb15ea98170cfb9202aab1f', 'b020819ac415976bd09a8e5bfdfb43642837c64d', 'e026384ffb3170a2e71053a4163db58b9bd8fba6', 'cc2aa96ae4063bdc50d692eaf5d11becbb94154b', '18b84d0404d891263420628e12a2ce4f2ac85806', 'f7db6031b84a136ad0e257df722b20faa7c37b8a', 'df507e56e2d782b347fe51537153c33b599da55f', '9dc332f5ca34b80af369646f767c40c6b189f831', '2047fc3715002899996ba3e41d16551d2067edad', 'a6c5a74fdbdce9a992b47706913c920902cda28c', '7692643f593ec6574f6b5c49302d11c8d4315335', '39a555ba94a6a5d851b31e0a7f07e19c48327835', '44edad5bb54ee23cb40b02180a84eb9edc052978', '7ed3748a462cd1ce7c30bb9a331b94a3cd79a401', '683d0bbc4ca7223e0ade55be58497af8aded6823', 'cfdd567955588e134770a9145ba57800ca88313c', '925c6281733662cd40fffaab54a6483b00f80ee6', 'ae570f59533ae941bbf5ab9ff5e739a5bd855fd6', '915c781243933fe1a127eda6178d3db467e0bcf3', '38b08dfd3384a04b73e236c83f1e3ce0fffeda45', '188b2bf2802c2bc5900a286a9ef023daaeb08c21', 'b62d8b975cf97b5c1328b9b03a05fa09b07cf13a', 'eee40200df5c68963c0b534f95b5154bfd60e290', 'badfbacdd09a9ee8821847f4b28d98625f354ed7', 'c9b6e19b3b37499de17da19e82b1b98e3b9f6b5c', 'ff7cbf264c0cb79ae56209ac381c51ef588ab97a', '5a52601691478857472333d8a13a07b09a7d13c5', '06f47c3b517f5c96c1bd981369843f3483947197', '2724cc99203d943c69f1fbddcb7b1ce16f62d287', '32024593947f8bb497f2f5d392e0d1d892a16ff3', 'dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd', '5d32f24cc3597760d3b846647e6a19dddc6e3d71', 'f191126550f155c50069a75a82fd44923997642a', '0507597597e1d2d9adaa33b87e5ea509e12fd2f0', '44b500b6795c4eb9f77362e25baee60fd83e5ce4', 'b7f20be809c47202ace4b54e69be5428012df1a9', '536cdf692fb15ef87b86da75af85e2f06c6cd52b', '3d2ac7cdbe89cdabfd95db9971de31878afa5498', 'a1f3c6d16061566a4f53c72a95e2679b8ee229e0', 'cda5da9b65f78b51cdfe5371f712a0d392dbdb4d', 'd62a42df2e24b7e12b7fae2ccd9c2931e62a95ae', '21a15c6b1f1ee80f163633ba617ad381f5edc0c1', '7a7ad9b422eac4ab9bfec6b5bbfc7c31dc02854c', '9a793897ec486df550367dee43078e463b234294', '5e399fe6f3aa65b42b9cdbf1c4c53f6989a570f0', 'e7c1e4a0a154db95304708eb1894447d3e3aa865', 'a971448f3f20ec52324b59e8e99f14da0e88efc6', '5bd556a49b0810eab2fe9c3c1fbbec7bca00769a', '56ab2f80343d7d842c1b0e3069833106d4fe3259', 'd9946a7ffc1c5e8265a62bd6868e6b0af3e977f4', '81d98993ada4d19501570644aa1c31a7722f970e', 'd23ce20bdfbe5a9598523961cb7cf747ce4f52ef', 'd9b139220ee253da673af44d58dc87bd184188f1', '43f39c2e602bc718609ba34ad15087810f76d382', '9b23d9305d2c35fc3438b38e886e9f862856aa3a', 'e3585a4cd5ddb5b8475a49c38143d18e7c640bfe', '896573f9bc2c6a695cb265df027c4f21214bc933', '22606a0a4d45dfb37b72a40de3728778ac4ffe84', 'e903f2cd9646639cbada753a705b49fb903e1add', '083e11a22ca9966ed010acdd5705351ab4300b52', 'fa43f1304414496523866a2e789cac11aba6d0c7', '7e149990bdd2a0010ad8c970e46427dbb3164071', 'c9a42c85e2c903329b3550181d230527e90e3646', '0fc029c6a47a7a930a2b078569de1173cdb547a4', '2fad1652942226454a44038f2350642817f9f74b', 'a6d8988179d214581ee63ed48eccda5be7393f98', 'c74c0f2facde2b392ab745144d6ad520575ab9ef', 'a3252629a37344be1f1f86a77cf685f9c147be4d', '31440b00f3ed8de65f368d41d6cf2efb07ca4a5c', '6e8d0d9850b05fc1de0ceaf77834e68939f782c1', '3c4472d701a7e9d9b8714a0b9d87ae190d1679fb', '4e1fcf18475d44721d90de00c7ce8b345394d956', 'beb0be5fe45d08ff45d6cdddfd86feac8500a7d7', '653baa669486cca928a7bafba8ec47626624fbdc', 'ef453dd1ad4d6388bd7a74dcb0c2d2573ee945a6', '396536bb5aa1ba78c71ea824d736640b615bda8a', '750a3893081fbe1e01f8c6eadf84726ace1eb47a', '7d580dc7a2ae0517032f28fe7ef62e0b96355527', '20028e24cca91422b8f02fdbf45d2cd9ef24c901', 'c958d8719dc2588bd27958b54a65dea514808796', '64ebbc577de757830b2f26ee3c8c7b1ddf10f86c', 'ea81e99e5980c807414651034a8c60426a158ca4', '59d30a06fc1371d01a853139053bd6bd9bb770a0', 'e71f68d6c91394db30bb1219ea882232f7be194d', '005416879e4c180e8e9dea94a08e57521ac8cf1d', '12be344fdd3b20f338ccab87933b89049ce202a4', 'e9fe2a2eeaf449cddd7770a1a7fa07bc75918c69', '69659b70fca10349d8c799d29e9115bb5ced64db', '8dec3e66163420ee0c2c259eef6e0c0f3185ca17', '87308167b19f6ffee76a144376cea540be028f0a', '4268e502ec403a049c6a01be93195236fddbaad7', '5234ddff5025501be2b4fca3ecba4e4eb584bbc5', '67b527460e155f1f5e850bb7fd65c7c373367b48', 'b36cf4ef977fb14bc57683630a9f3f3680705550', 'd0b7677c2c9a8265643a90175f86987521615849', '4c7e0a9fa66fd1becc590d6060228b0305dd400b', 'ad76497b12b0d81a98dc6e230e5516adffbfa0ee', 'e9f358eef6a5f48530ecfe0ca6fca689410b499c', 'd293cd0735e050325eb7d2eafec435ec8aec92eb', 'b49013b73efa25466652d8d8122974e60c927ec4', '01b91af59cb08b843470742a7a25d1f4239ebf45', 'e1dc6d9f3493eb35e3d3eef1d70c2d1fc94d74c2', '1c402297d60152722cf5544a14ca5271d576d55e', '9e5ca8416de6011fa8c0d63430e9b89fe751ee2b', '98febb989abd1bdb624420f21122c477f2614a08', '65efb981347d6f81fb2713cd27cdfdfa9d8781b9', '079e5d647ce39cf2ab5b5f37c5ce28b59fb6db13', '6263197a62389c9dce0210cd9d65a6e601345edc']\n",
            "\n",
            "\n",
            "Convert list_comits_with_critical_files para um conjunto de commits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.4 Commits contendo SATD Keywords e contendo Arquivos Críticos\n",
        "\n",
        "Faz a intersecção entre commits com SATD keywords e commits com arquivos críticos"
      ],
      "metadata": {
        "id": "XEye67PhjPUf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "set_commits_with_satd_keywords_and_critical_files = set_comits_with_critical_files.intersection(set_list_commit_with_satd_keywords)\n",
        "list_commits_with_satd_keywords_and_critical_files = list(set_commits_with_satd_keywords_and_critical_files)\n",
        "\n",
        "df_commits_with_satd_keywords1 = df_commit_diffs_aux[df_commit_diffs_aux['commits'].isin(list_commits_with_satd_keywords_and_critical_files)]\n",
        "\n",
        "df_commits_with_satd_keywords1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-G4kwyDxjQyL",
        "outputId": "a8242ccc-bdfe-4af2-e6ab-87a5d90381f0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 125 entries, 36 to 312\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   commits   125 non-null    object\n",
            " 1   keywords  125 non-null    object\n",
            " 2   comments  125 non-null    object\n",
            "dtypes: object(3)\n",
            "memory usage: 3.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_commits_with_satd_keywords1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "gI-wEHH-jU52",
        "outputId": "9c46571d-3301-40dd-f203-f007cf897f57"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      commits  \\\n",
              "36   269d16d3c915d09f650ae32aa81542bd8522ca68   \n",
              "41   5e2a9a560d847bd0cf364d86bd6784f70d99c71a   \n",
              "43   c24740c7b0f6a6e7c66659da786a346650b76766   \n",
              "49   0699ff2ce60abb466cab5315977a224f1a70a4da   \n",
              "50   1c6d5bbac672cbf49591aed0889510b10e3285fa   \n",
              "..                                        ...   \n",
              "285  06f47c3b517f5c96c1bd981369843f3483947197   \n",
              "287  dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd   \n",
              "300  896573f9bc2c6a695cb265df027c4f21214bc933   \n",
              "303  2fad1652942226454a44038f2350642817f9f74b   \n",
              "312  750a3893081fbe1e01f8c6eadf84726ace1eb47a   \n",
              "\n",
              "                                       keywords  \\\n",
              "36               [TODO, TODO, TODO, TODO, TODO]   \n",
              "41   [TODO, TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "43         [TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "49               [TODO, TODO, TODO, TODO, TODO]   \n",
              "50         [TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "..                                          ...   \n",
              "285                          [TODO, TODO, TODO]   \n",
              "287              [TODO, TODO, TODO, TODO, TODO]   \n",
              "300                                      [TODO]   \n",
              "303                          [TODO, TODO, TODO]   \n",
              "312                                      [TODO]   \n",
              "\n",
              "                                              comments  \n",
              "36   [        /* TODO: add docs */,                ...  \n",
              "41   [    // TODO: errorCode 4 for InvalidFetchSize...  \n",
              "43   [        /* TODO: add config docs */,         ...  \n",
              "49   [            // TODO: list offset call could b...  \n",
              "50   [        // TODO read uncompressed content siz...  \n",
              "..                                                 ...  \n",
              "285  [     * TODO: we pass in the new input partiti...  \n",
              "287  [    // TODO: convert to StreamTask when we re...  \n",
              "300  [            // However, values are also use a...  \n",
              "303  [            // TODO (?) If we cannot fetch re...  \n",
              "312  [    // TODO: We would still return true if al...  \n",
              "\n",
              "[125 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-14ff79ac-70f5-4b25-995a-6dd8947662aa\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>commits</th>\n",
              "      <th>keywords</th>\n",
              "      <th>comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>269d16d3c915d09f650ae32aa81542bd8522ca68</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        /* TODO: add docs */,                ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>5e2a9a560d847bd0cf364d86bd6784f70d99c71a</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[    // TODO: errorCode 4 for InvalidFetchSize...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>c24740c7b0f6a6e7c66659da786a346650b76766</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        /* TODO: add config docs */,         ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0699ff2ce60abb466cab5315977a224f1a70a4da</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[            // TODO: list offset call could b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>1c6d5bbac672cbf49591aed0889510b10e3285fa</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        // TODO read uncompressed content siz...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>06f47c3b517f5c96c1bd981369843f3483947197</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[     * TODO: we pass in the new input partiti...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[    // TODO: convert to StreamTask when we re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>896573f9bc2c6a695cb265df027c4f21214bc933</td>\n",
              "      <td>[TODO]</td>\n",
              "      <td>[            // However, values are also use a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>303</th>\n",
              "      <td>2fad1652942226454a44038f2350642817f9f74b</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[            // TODO (?) If we cannot fetch re...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>312</th>\n",
              "      <td>750a3893081fbe1e01f8c6eadf84726ace1eb47a</td>\n",
              "      <td>[TODO]</td>\n",
              "      <td>[    // TODO: We would still return true if al...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>125 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14ff79ac-70f5-4b25-995a-6dd8947662aa')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-14ff79ac-70f5-4b25-995a-6dd8947662aa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-14ff79ac-70f5-4b25-995a-6dd8947662aa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a80dd963-f126-4f2d-9000-b04f1aec2fe9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a80dd963-f126-4f2d-9000-b04f1aec2fe9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a80dd963-f126-4f2d-9000-b04f1aec2fe9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_commits_with_satd_keywords2 = df_commits_to_analyze_aux[df_commits_to_analyze_aux['commits'].isin(list_commits_with_satd_keywords_and_critical_files)]\n",
        "df_commits_with_satd_keywords2.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P39IdMRqjXyl",
        "outputId": "8474068f-fdf4-485d-a82b-48c9b661291c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 125 entries, 0 to 1420\n",
            "Data columns (total 9 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   commits              125 non-null    object\n",
            " 1   msg                  125 non-null    object\n",
            " 2   critical_files       125 non-null    object\n",
            " 3   data                 125 non-null    object\n",
            " 4   lines                125 non-null    int64 \n",
            " 5   files                125 non-null    int64 \n",
            " 6   diff_commit          125 non-null    object\n",
            " 7   modified_files       125 non-null    object\n",
            " 8   dict_modified_files  125 non-null    object\n",
            "dtypes: int64(2), object(7)\n",
            "memory usage: 9.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_commits_with_satd_keywords2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753
        },
        "id": "gD9Z405FjaxI",
        "outputId": "c86c4d29-a87d-4d08-aa67-8cb1d7ff1bd4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       commits  \\\n",
              "0     269d16d3c915d09f650ae32aa81542bd8522ca68   \n",
              "6     5e2a9a560d847bd0cf364d86bd6784f70d99c71a   \n",
              "14    c24740c7b0f6a6e7c66659da786a346650b76766   \n",
              "27    0699ff2ce60abb466cab5315977a224f1a70a4da   \n",
              "28    1c6d5bbac672cbf49591aed0889510b10e3285fa   \n",
              "...                                        ...   \n",
              "1371  06f47c3b517f5c96c1bd981369843f3483947197   \n",
              "1374  dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd   \n",
              "1400  896573f9bc2c6a695cb265df027c4f21214bc933   \n",
              "1408  2fad1652942226454a44038f2350642817f9f74b   \n",
              "1420  750a3893081fbe1e01f8c6eadf84726ace1eb47a   \n",
              "\n",
              "                                                    msg  \\\n",
              "0                              KAFKA-1227 New producer!   \n",
              "6     KAFKA-1260 Integration Test for New Producer P...   \n",
              "14    KAFKA-1328 New consumer APIs; reviewed by Jun ...   \n",
              "27                            KAFKA-1760: New consumer.   \n",
              "28            KAFKA-1915: Add checkstyle for java code.   \n",
              "...                                                 ...   \n",
              "1371  KAFKA-10199: Further refactor task lifecycle m...   \n",
              "1374  KAFKA-10199: Handle task closure and recycling...   \n",
              "1400  KAFKA-14279: Add 3.3.x streams system tests (#...   \n",
              "1408  KAFKA-10199: Add task updater metrics, part 1 ...   \n",
              "1420  MINOR: Follow-up on failing streams test, and ...   \n",
              "\n",
              "                                critical_files       data  lines  files  \\\n",
              "0                         [KafkaProducer.java]  28/1/2014  11720    121   \n",
              "6                         [KafkaProducer.java]  27/2/2014    507     14   \n",
              "14    [KafkaConsumer.java, KafkaProducer.java]  20/5/2014   1744     11   \n",
              "27    [KafkaConsumer.java, KafkaProducer.java]  29/1/2015   5931     89   \n",
              "28    [KafkaConsumer.java, KafkaProducer.java]   3/2/2015   5440     97   \n",
              "...                                        ...        ...    ...    ...   \n",
              "1371                         [StreamTask.java]  27/7/2022   1065     18   \n",
              "1374                         [StreamTask.java]  15/8/2022    667     11   \n",
              "1400                      [StreamsConfig.java]   9/1/2023   1365     14   \n",
              "1408                       [StreamThread.java]  24/2/2023    509      9   \n",
              "1420                         [StreamTask.java]  12/4/2023    160     14   \n",
              "\n",
              "                                            diff_commit  \\\n",
              "0     [{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...   \n",
              "6     [{'KafkaProducer.java': '@@ -33,6 +33,7 @@ imp...   \n",
              "14    [{'KafkaConsumer.java': '@@ -0,0 +1,575 @@\n",
              "+/*...   \n",
              "27    [{'KafkaConsumer.java': '@@ -9,380 +9,447 @@\n",
              " ...   \n",
              "28    [{'KafkaConsumer.java': '@@ -30,13 +30,13 @@ i...   \n",
              "...                                                 ...   \n",
              "1371  [{'StreamTask.java': '@@ -83,7 +83,6 @@ public...   \n",
              "1374  [{'StreamTask.java': '@@ -568,7 +568,7 @@ publ...   \n",
              "1400  [{'StreamsConfig.java': '@@ -392,6 +392,12 @@ ...   \n",
              "1408  [{'StreamThread.java': '@@ -404,7 +404,7 @@ pu...   \n",
              "1420  [{'StreamTask.java': '@@ -219,13 +219,13 @@ pu...   \n",
              "\n",
              "                                         modified_files  \\\n",
              "0     [build.sbt, BufferExhaustedException.java, Cal...   \n",
              "6     [KafkaProducer.java, MockProducer.java, Record...   \n",
              "14    [Consumer.java, ConsumerConfig.java, ConsumerR...   \n",
              "27    [build.gradle, ClientRequest.java, ClusterConn...   \n",
              "28    [README.md, build.gradle, checkstyle.xml, impo...   \n",
              "...                                                 ...   \n",
              "1371  [AbstractTask.java, ActiveTaskCreator.java, St...   \n",
              "1374  [ActiveTaskCreator.java, StandbyTask.java, Sta...   \n",
              "1400  [build.gradle, dependencies.gradle, settings.g...   \n",
              "1408  [ChangelogReader.java, DefaultStateUpdater.jav...   \n",
              "1420  [DefaultStateUpdater.java, ReadOnlyTask.java, ...   \n",
              "\n",
              "                                    dict_modified_files  \n",
              "0     [@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...  \n",
              "6     [@@ -33,6 +33,7 @@ import org.apache.kafka.com...  \n",
              "14    [@@ -0,0 +1,125 @@\\n+/**\\n+ * Licensed to the ...  \n",
              "27    [@@ -369,6 +369,7 @@ project(':clients') {\\n  ...  \n",
              "28    [@@ -98,6 +98,9 @@ Please note for this to wor...  \n",
              "...                                                 ...  \n",
              "1371  [@@ -43,10 +43,11 @@ public abstract class Abs...  \n",
              "1374  [@@ -135,6 +135,7 @@ class ActiveTaskCreator {...  \n",
              "1400  [@@ -2040,6 +2040,7 @@ project(':streams') {\\n...  \n",
              "1408  [@@ -28,8 +28,10 @@ import java.util.Set;\\n pu...  \n",
              "1420  [@@ -748,7 +748,7 @@ public class DefaultState...  \n",
              "\n",
              "[125 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c10b7aa8-6052-478d-832d-e6a81ed58b3e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>commits</th>\n",
              "      <th>msg</th>\n",
              "      <th>critical_files</th>\n",
              "      <th>data</th>\n",
              "      <th>lines</th>\n",
              "      <th>files</th>\n",
              "      <th>diff_commit</th>\n",
              "      <th>modified_files</th>\n",
              "      <th>dict_modified_files</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>269d16d3c915d09f650ae32aa81542bd8522ca68</td>\n",
              "      <td>KAFKA-1227 New producer!</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>28/1/2014</td>\n",
              "      <td>11720</td>\n",
              "      <td>121</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...</td>\n",
              "      <td>[build.sbt, BufferExhaustedException.java, Cal...</td>\n",
              "      <td>[@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5e2a9a560d847bd0cf364d86bd6784f70d99c71a</td>\n",
              "      <td>KAFKA-1260 Integration Test for New Producer P...</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>27/2/2014</td>\n",
              "      <td>507</td>\n",
              "      <td>14</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -33,6 +33,7 @@ imp...</td>\n",
              "      <td>[KafkaProducer.java, MockProducer.java, Record...</td>\n",
              "      <td>[@@ -33,6 +33,7 @@ import org.apache.kafka.com...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>c24740c7b0f6a6e7c66659da786a346650b76766</td>\n",
              "      <td>KAFKA-1328 New consumer APIs; reviewed by Jun ...</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>20/5/2014</td>\n",
              "      <td>1744</td>\n",
              "      <td>11</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -0,0 +1,575 @@\n",
              "+/*...</td>\n",
              "      <td>[Consumer.java, ConsumerConfig.java, ConsumerR...</td>\n",
              "      <td>[@@ -0,0 +1,125 @@\\n+/**\\n+ * Licensed to the ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0699ff2ce60abb466cab5315977a224f1a70a4da</td>\n",
              "      <td>KAFKA-1760: New consumer.</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>29/1/2015</td>\n",
              "      <td>5931</td>\n",
              "      <td>89</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -9,380 +9,447 @@\n",
              " ...</td>\n",
              "      <td>[build.gradle, ClientRequest.java, ClusterConn...</td>\n",
              "      <td>[@@ -369,6 +369,7 @@ project(':clients') {\\n  ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1c6d5bbac672cbf49591aed0889510b10e3285fa</td>\n",
              "      <td>KAFKA-1915: Add checkstyle for java code.</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>3/2/2015</td>\n",
              "      <td>5440</td>\n",
              "      <td>97</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -30,13 +30,13 @@ i...</td>\n",
              "      <td>[README.md, build.gradle, checkstyle.xml, impo...</td>\n",
              "      <td>[@@ -98,6 +98,9 @@ Please note for this to wor...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1371</th>\n",
              "      <td>06f47c3b517f5c96c1bd981369843f3483947197</td>\n",
              "      <td>KAFKA-10199: Further refactor task lifecycle m...</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>27/7/2022</td>\n",
              "      <td>1065</td>\n",
              "      <td>18</td>\n",
              "      <td>[{'StreamTask.java': '@@ -83,7 +83,6 @@ public...</td>\n",
              "      <td>[AbstractTask.java, ActiveTaskCreator.java, St...</td>\n",
              "      <td>[@@ -43,10 +43,11 @@ public abstract class Abs...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1374</th>\n",
              "      <td>dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd</td>\n",
              "      <td>KAFKA-10199: Handle task closure and recycling...</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>15/8/2022</td>\n",
              "      <td>667</td>\n",
              "      <td>11</td>\n",
              "      <td>[{'StreamTask.java': '@@ -568,7 +568,7 @@ publ...</td>\n",
              "      <td>[ActiveTaskCreator.java, StandbyTask.java, Sta...</td>\n",
              "      <td>[@@ -135,6 +135,7 @@ class ActiveTaskCreator {...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1400</th>\n",
              "      <td>896573f9bc2c6a695cb265df027c4f21214bc933</td>\n",
              "      <td>KAFKA-14279: Add 3.3.x streams system tests (#...</td>\n",
              "      <td>[StreamsConfig.java]</td>\n",
              "      <td>9/1/2023</td>\n",
              "      <td>1365</td>\n",
              "      <td>14</td>\n",
              "      <td>[{'StreamsConfig.java': '@@ -392,6 +392,12 @@ ...</td>\n",
              "      <td>[build.gradle, dependencies.gradle, settings.g...</td>\n",
              "      <td>[@@ -2040,6 +2040,7 @@ project(':streams') {\\n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1408</th>\n",
              "      <td>2fad1652942226454a44038f2350642817f9f74b</td>\n",
              "      <td>KAFKA-10199: Add task updater metrics, part 1 ...</td>\n",
              "      <td>[StreamThread.java]</td>\n",
              "      <td>24/2/2023</td>\n",
              "      <td>509</td>\n",
              "      <td>9</td>\n",
              "      <td>[{'StreamThread.java': '@@ -404,7 +404,7 @@ pu...</td>\n",
              "      <td>[ChangelogReader.java, DefaultStateUpdater.jav...</td>\n",
              "      <td>[@@ -28,8 +28,10 @@ import java.util.Set;\\n pu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1420</th>\n",
              "      <td>750a3893081fbe1e01f8c6eadf84726ace1eb47a</td>\n",
              "      <td>MINOR: Follow-up on failing streams test, and ...</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>12/4/2023</td>\n",
              "      <td>160</td>\n",
              "      <td>14</td>\n",
              "      <td>[{'StreamTask.java': '@@ -219,13 +219,13 @@ pu...</td>\n",
              "      <td>[DefaultStateUpdater.java, ReadOnlyTask.java, ...</td>\n",
              "      <td>[@@ -748,7 +748,7 @@ public class DefaultState...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>125 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c10b7aa8-6052-478d-832d-e6a81ed58b3e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c10b7aa8-6052-478d-832d-e6a81ed58b3e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c10b7aa8-6052-478d-832d-e6a81ed58b3e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ae28afc6-d5b9-4ba4-a8a5-c839e463e4cf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ae28afc6-d5b9-4ba4-a8a5-c839e463e4cf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ae28afc6-d5b9-4ba4-a8a5-c839e463e4cf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_commits_with_satd_keywords_with_critical_files = df_commits_with_satd_keywords1.merge(df_commits_with_satd_keywords2, on='commits', how='outer')\n",
        "\n",
        "colunas_ok = ['commits', 'data', 'msg', 'lines', 'files', 'critical_files', 'keywords', 'comments',\n",
        "       'diff_commit', 'modified_files','dict_modified_files']\n",
        "\n",
        "df_commits_with_satd_keywords_with_critical_files = df_commits_with_satd_keywords_with_critical_files[colunas_ok]\n",
        "df_commits_with_satd_keywords_with_critical_files.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fWrp5LpjdmW",
        "outputId": "e77a729a-537f-4670-96f2-497fbeee273a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 125 entries, 0 to 124\n",
            "Data columns (total 11 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   commits              125 non-null    object\n",
            " 1   data                 125 non-null    object\n",
            " 2   msg                  125 non-null    object\n",
            " 3   lines                125 non-null    int64 \n",
            " 4   files                125 non-null    int64 \n",
            " 5   critical_files       125 non-null    object\n",
            " 6   keywords             125 non-null    object\n",
            " 7   comments             125 non-null    object\n",
            " 8   diff_commit          125 non-null    object\n",
            " 9   modified_files       125 non-null    object\n",
            " 10  dict_modified_files  125 non-null    object\n",
            "dtypes: int64(2), object(9)\n",
            "memory usage: 11.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_commits_with_satd_keywords_with_critical_files"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GAVfNkjxjglm",
        "outputId": "e7036f1a-68e5-484d-b1e6-e4fc08b527fc"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      commits       data  \\\n",
              "0    269d16d3c915d09f650ae32aa81542bd8522ca68  28/1/2014   \n",
              "1    5e2a9a560d847bd0cf364d86bd6784f70d99c71a  27/2/2014   \n",
              "2    c24740c7b0f6a6e7c66659da786a346650b76766  20/5/2014   \n",
              "3    0699ff2ce60abb466cab5315977a224f1a70a4da  29/1/2015   \n",
              "4    1c6d5bbac672cbf49591aed0889510b10e3285fa   3/2/2015   \n",
              "..                                        ...        ...   \n",
              "120  06f47c3b517f5c96c1bd981369843f3483947197  27/7/2022   \n",
              "121  dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd  15/8/2022   \n",
              "122  896573f9bc2c6a695cb265df027c4f21214bc933   9/1/2023   \n",
              "123  2fad1652942226454a44038f2350642817f9f74b  24/2/2023   \n",
              "124  750a3893081fbe1e01f8c6eadf84726ace1eb47a  12/4/2023   \n",
              "\n",
              "                                                   msg  lines  files  \\\n",
              "0                             KAFKA-1227 New producer!  11720    121   \n",
              "1    KAFKA-1260 Integration Test for New Producer P...    507     14   \n",
              "2    KAFKA-1328 New consumer APIs; reviewed by Jun ...   1744     11   \n",
              "3                            KAFKA-1760: New consumer.   5931     89   \n",
              "4            KAFKA-1915: Add checkstyle for java code.   5440     97   \n",
              "..                                                 ...    ...    ...   \n",
              "120  KAFKA-10199: Further refactor task lifecycle m...   1065     18   \n",
              "121  KAFKA-10199: Handle task closure and recycling...    667     11   \n",
              "122  KAFKA-14279: Add 3.3.x streams system tests (#...   1365     14   \n",
              "123  KAFKA-10199: Add task updater metrics, part 1 ...    509      9   \n",
              "124  MINOR: Follow-up on failing streams test, and ...    160     14   \n",
              "\n",
              "                               critical_files  \\\n",
              "0                        [KafkaProducer.java]   \n",
              "1                        [KafkaProducer.java]   \n",
              "2    [KafkaConsumer.java, KafkaProducer.java]   \n",
              "3    [KafkaConsumer.java, KafkaProducer.java]   \n",
              "4    [KafkaConsumer.java, KafkaProducer.java]   \n",
              "..                                        ...   \n",
              "120                         [StreamTask.java]   \n",
              "121                         [StreamTask.java]   \n",
              "122                      [StreamsConfig.java]   \n",
              "123                       [StreamThread.java]   \n",
              "124                         [StreamTask.java]   \n",
              "\n",
              "                                       keywords  \\\n",
              "0                [TODO, TODO, TODO, TODO, TODO]   \n",
              "1    [TODO, TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "2          [TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "3                [TODO, TODO, TODO, TODO, TODO]   \n",
              "4          [TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "..                                          ...   \n",
              "120                          [TODO, TODO, TODO]   \n",
              "121              [TODO, TODO, TODO, TODO, TODO]   \n",
              "122                                      [TODO]   \n",
              "123                          [TODO, TODO, TODO]   \n",
              "124                                      [TODO]   \n",
              "\n",
              "                                              comments  \\\n",
              "0    [        /* TODO: add docs */,                ...   \n",
              "1    [    // TODO: errorCode 4 for InvalidFetchSize...   \n",
              "2    [        /* TODO: add config docs */,         ...   \n",
              "3    [            // TODO: list offset call could b...   \n",
              "4    [        // TODO read uncompressed content siz...   \n",
              "..                                                 ...   \n",
              "120  [     * TODO: we pass in the new input partiti...   \n",
              "121  [    // TODO: convert to StreamTask when we re...   \n",
              "122  [            // However, values are also use a...   \n",
              "123  [            // TODO (?) If we cannot fetch re...   \n",
              "124  [    // TODO: We would still return true if al...   \n",
              "\n",
              "                                           diff_commit  \\\n",
              "0    [{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...   \n",
              "1    [{'KafkaProducer.java': '@@ -33,6 +33,7 @@ imp...   \n",
              "2    [{'KafkaConsumer.java': '@@ -0,0 +1,575 @@\n",
              "+/*...   \n",
              "3    [{'KafkaConsumer.java': '@@ -9,380 +9,447 @@\n",
              " ...   \n",
              "4    [{'KafkaConsumer.java': '@@ -30,13 +30,13 @@ i...   \n",
              "..                                                 ...   \n",
              "120  [{'StreamTask.java': '@@ -83,7 +83,6 @@ public...   \n",
              "121  [{'StreamTask.java': '@@ -568,7 +568,7 @@ publ...   \n",
              "122  [{'StreamsConfig.java': '@@ -392,6 +392,12 @@ ...   \n",
              "123  [{'StreamThread.java': '@@ -404,7 +404,7 @@ pu...   \n",
              "124  [{'StreamTask.java': '@@ -219,13 +219,13 @@ pu...   \n",
              "\n",
              "                                        modified_files  \\\n",
              "0    [build.sbt, BufferExhaustedException.java, Cal...   \n",
              "1    [KafkaProducer.java, MockProducer.java, Record...   \n",
              "2    [Consumer.java, ConsumerConfig.java, ConsumerR...   \n",
              "3    [build.gradle, ClientRequest.java, ClusterConn...   \n",
              "4    [README.md, build.gradle, checkstyle.xml, impo...   \n",
              "..                                                 ...   \n",
              "120  [AbstractTask.java, ActiveTaskCreator.java, St...   \n",
              "121  [ActiveTaskCreator.java, StandbyTask.java, Sta...   \n",
              "122  [build.gradle, dependencies.gradle, settings.g...   \n",
              "123  [ChangelogReader.java, DefaultStateUpdater.jav...   \n",
              "124  [DefaultStateUpdater.java, ReadOnlyTask.java, ...   \n",
              "\n",
              "                                   dict_modified_files  \n",
              "0    [@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...  \n",
              "1    [@@ -33,6 +33,7 @@ import org.apache.kafka.com...  \n",
              "2    [@@ -0,0 +1,125 @@\\n+/**\\n+ * Licensed to the ...  \n",
              "3    [@@ -369,6 +369,7 @@ project(':clients') {\\n  ...  \n",
              "4    [@@ -98,6 +98,9 @@ Please note for this to wor...  \n",
              "..                                                 ...  \n",
              "120  [@@ -43,10 +43,11 @@ public abstract class Abs...  \n",
              "121  [@@ -135,6 +135,7 @@ class ActiveTaskCreator {...  \n",
              "122  [@@ -2040,6 +2040,7 @@ project(':streams') {\\n...  \n",
              "123  [@@ -28,8 +28,10 @@ import java.util.Set;\\n pu...  \n",
              "124  [@@ -748,7 +748,7 @@ public class DefaultState...  \n",
              "\n",
              "[125 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f3259ef-ccaf-4bc1-b6ea-e2f819480c37\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>commits</th>\n",
              "      <th>data</th>\n",
              "      <th>msg</th>\n",
              "      <th>lines</th>\n",
              "      <th>files</th>\n",
              "      <th>critical_files</th>\n",
              "      <th>keywords</th>\n",
              "      <th>comments</th>\n",
              "      <th>diff_commit</th>\n",
              "      <th>modified_files</th>\n",
              "      <th>dict_modified_files</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>269d16d3c915d09f650ae32aa81542bd8522ca68</td>\n",
              "      <td>28/1/2014</td>\n",
              "      <td>KAFKA-1227 New producer!</td>\n",
              "      <td>11720</td>\n",
              "      <td>121</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        /* TODO: add docs */,                ...</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...</td>\n",
              "      <td>[build.sbt, BufferExhaustedException.java, Cal...</td>\n",
              "      <td>[@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5e2a9a560d847bd0cf364d86bd6784f70d99c71a</td>\n",
              "      <td>27/2/2014</td>\n",
              "      <td>KAFKA-1260 Integration Test for New Producer P...</td>\n",
              "      <td>507</td>\n",
              "      <td>14</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[    // TODO: errorCode 4 for InvalidFetchSize...</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -33,6 +33,7 @@ imp...</td>\n",
              "      <td>[KafkaProducer.java, MockProducer.java, Record...</td>\n",
              "      <td>[@@ -33,6 +33,7 @@ import org.apache.kafka.com...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>c24740c7b0f6a6e7c66659da786a346650b76766</td>\n",
              "      <td>20/5/2014</td>\n",
              "      <td>KAFKA-1328 New consumer APIs; reviewed by Jun ...</td>\n",
              "      <td>1744</td>\n",
              "      <td>11</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        /* TODO: add config docs */,         ...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -0,0 +1,575 @@\n",
              "+/*...</td>\n",
              "      <td>[Consumer.java, ConsumerConfig.java, ConsumerR...</td>\n",
              "      <td>[@@ -0,0 +1,125 @@\\n+/**\\n+ * Licensed to the ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0699ff2ce60abb466cab5315977a224f1a70a4da</td>\n",
              "      <td>29/1/2015</td>\n",
              "      <td>KAFKA-1760: New consumer.</td>\n",
              "      <td>5931</td>\n",
              "      <td>89</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[            // TODO: list offset call could b...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -9,380 +9,447 @@\n",
              " ...</td>\n",
              "      <td>[build.gradle, ClientRequest.java, ClusterConn...</td>\n",
              "      <td>[@@ -369,6 +369,7 @@ project(':clients') {\\n  ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1c6d5bbac672cbf49591aed0889510b10e3285fa</td>\n",
              "      <td>3/2/2015</td>\n",
              "      <td>KAFKA-1915: Add checkstyle for java code.</td>\n",
              "      <td>5440</td>\n",
              "      <td>97</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        // TODO read uncompressed content siz...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -30,13 +30,13 @@ i...</td>\n",
              "      <td>[README.md, build.gradle, checkstyle.xml, impo...</td>\n",
              "      <td>[@@ -98,6 +98,9 @@ Please note for this to wor...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>06f47c3b517f5c96c1bd981369843f3483947197</td>\n",
              "      <td>27/7/2022</td>\n",
              "      <td>KAFKA-10199: Further refactor task lifecycle m...</td>\n",
              "      <td>1065</td>\n",
              "      <td>18</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[     * TODO: we pass in the new input partiti...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -83,7 +83,6 @@ public...</td>\n",
              "      <td>[AbstractTask.java, ActiveTaskCreator.java, St...</td>\n",
              "      <td>[@@ -43,10 +43,11 @@ public abstract class Abs...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd</td>\n",
              "      <td>15/8/2022</td>\n",
              "      <td>KAFKA-10199: Handle task closure and recycling...</td>\n",
              "      <td>667</td>\n",
              "      <td>11</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[    // TODO: convert to StreamTask when we re...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -568,7 +568,7 @@ publ...</td>\n",
              "      <td>[ActiveTaskCreator.java, StandbyTask.java, Sta...</td>\n",
              "      <td>[@@ -135,6 +135,7 @@ class ActiveTaskCreator {...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>896573f9bc2c6a695cb265df027c4f21214bc933</td>\n",
              "      <td>9/1/2023</td>\n",
              "      <td>KAFKA-14279: Add 3.3.x streams system tests (#...</td>\n",
              "      <td>1365</td>\n",
              "      <td>14</td>\n",
              "      <td>[StreamsConfig.java]</td>\n",
              "      <td>[TODO]</td>\n",
              "      <td>[            // However, values are also use a...</td>\n",
              "      <td>[{'StreamsConfig.java': '@@ -392,6 +392,12 @@ ...</td>\n",
              "      <td>[build.gradle, dependencies.gradle, settings.g...</td>\n",
              "      <td>[@@ -2040,6 +2040,7 @@ project(':streams') {\\n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>2fad1652942226454a44038f2350642817f9f74b</td>\n",
              "      <td>24/2/2023</td>\n",
              "      <td>KAFKA-10199: Add task updater metrics, part 1 ...</td>\n",
              "      <td>509</td>\n",
              "      <td>9</td>\n",
              "      <td>[StreamThread.java]</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[            // TODO (?) If we cannot fetch re...</td>\n",
              "      <td>[{'StreamThread.java': '@@ -404,7 +404,7 @@ pu...</td>\n",
              "      <td>[ChangelogReader.java, DefaultStateUpdater.jav...</td>\n",
              "      <td>[@@ -28,8 +28,10 @@ import java.util.Set;\\n pu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>750a3893081fbe1e01f8c6eadf84726ace1eb47a</td>\n",
              "      <td>12/4/2023</td>\n",
              "      <td>MINOR: Follow-up on failing streams test, and ...</td>\n",
              "      <td>160</td>\n",
              "      <td>14</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>[TODO]</td>\n",
              "      <td>[    // TODO: We would still return true if al...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -219,13 +219,13 @@ pu...</td>\n",
              "      <td>[DefaultStateUpdater.java, ReadOnlyTask.java, ...</td>\n",
              "      <td>[@@ -748,7 +748,7 @@ public class DefaultState...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>125 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f3259ef-ccaf-4bc1-b6ea-e2f819480c37')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5f3259ef-ccaf-4bc1-b6ea-e2f819480c37 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5f3259ef-ccaf-4bc1-b6ea-e2f819480c37');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cc2483f7-d32d-4225-abaf-68f37b7c345b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cc2483f7-d32d-4225-abaf-68f37b7c345b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cc2483f7-d32d-4225-abaf-68f37b7c345b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(df_commits_with_satd_keywords_with_critical_files.describe(), 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Wz-UmtmgU9sB",
        "outputId": "e3774596-7e07-4132-de26-a458210ffdb2"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          lines   files\n",
              "count    125.00  125.00\n",
              "mean    1680.73   28.78\n",
              "std     2435.35   27.64\n",
              "min        4.00    1.00\n",
              "25%      386.00   10.00\n",
              "50%      949.00   19.00\n",
              "75%     1872.00   38.00\n",
              "max    16103.00  143.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9bf9fdf9-db55-4d93-bd91-d71358c2b942\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lines</th>\n",
              "      <th>files</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>125.00</td>\n",
              "      <td>125.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1680.73</td>\n",
              "      <td>28.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2435.35</td>\n",
              "      <td>27.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>386.00</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>949.00</td>\n",
              "      <td>19.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1872.00</td>\n",
              "      <td>38.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>16103.00</td>\n",
              "      <td>143.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9bf9fdf9-db55-4d93-bd91-d71358c2b942')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9bf9fdf9-db55-4d93-bd91-d71358c2b942 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9bf9fdf9-db55-4d93-bd91-d71358c2b942');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0034e413-7b24-408f-9b07-ef3375b4aac6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0034e413-7b24-408f-9b07-ef3375b4aac6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0034e413-7b24-408f-9b07-ef3375b4aac6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "qtd_commits_with_satd_and_critical_files = len(df_commits_with_satd_keywords_with_critical_files.commits.to_list())\n",
        "qtd_commits_no_satd_no_critical_files = my_qtd_commits_to_analyze - qtd_commits_with_satd_and_critical_files\n",
        "\n",
        "# Dos x commits com arquivos criticos quantos tem SATD?\n",
        "print(f'Total de commits analisados: {my_qtd_commits_to_analyze}')\n",
        "print(f'Commits que possuem pelo menos um arquivo crítico: {qtd_commits_with_critical_files}')\n",
        "print(f'Qtd de commits sem SATD e sem arquivos críticos: {qtd_commits_no_satd_no_critical_files}')\n",
        "print(f'Qtd de commits com SATD e arquivos críticos: {qtd_commits_with_satd_and_critical_files}')\n",
        "print(f'\\n')\n",
        "\n",
        "plt.pie([qtd_commits_no_satd_no_critical_files, qtd_commits_with_satd_and_critical_files], labels=[f\"No SATD and NO Critical Files ({qtd_commits_no_satd_no_critical_files})\", f\"With SATD and With Critical Files ({qtd_commits_with_satd_and_critical_files})\"], autopct=\"%1.1f%%\")\n",
        "plt.title(f\"Total of Commits Analyzed: {my_qtd_commits_to_analyze}\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "aFS8pxRPjjWE",
        "outputId": "9bb32f19-e5b6-405f-d809-2d781606022d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total de commits analisados: 11732\n",
            "Commits que possuem pelo menos um arquivo crítico: 1452\n",
            "Qtd de commits sem SATD e sem arquivos críticos: 11607\n",
            "Qtd de commits com SATD e arquivos críticos: 125\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Salva o dataframe de df_commits_with_satd_keywords_with_critical_files em uma planilha excel')\n",
        "df_commits_with_satd_keywords_with_critical_files.to_excel('commits_with_satd_keywords_with_critical_files.xlsx')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60H0Gcczjr22",
        "outputId": "3137e5d5-771f-49ae-bb90-13f9a38e990c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Salva o dataframe de df_commits_with_satd_keywords_with_critical_files em uma planilha excel\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Analyzes Jira issues"
      ],
      "metadata": {
        "id": "je_qRA1qjyrU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.1 Configures the instance, project and date range"
      ],
      "metadata": {
        "id": "eN7n-r-yj3wA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the Jira connection\n",
        "print('Initialize the Jira connection')\n",
        "jira = JIRA(JIRA_SERVER, basic_auth=(username, password))\n",
        "\n",
        "# Create a JiraUtils instance\n",
        "print('Create a JiraUtils instance')\n",
        "jira_utils = JiraUtils('KAFKA', jira)\n",
        "# 2011, 8, 1\n",
        "date1 = (2011, 8, 1)\n",
        "date2 = (2023, 10, 4)\n",
        "distance = 120\n",
        "print('Define date intervals')\n",
        "print(f'From: {str(date1)} to: {date2}, by: {distance} days of distance.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-FzsjCHsj2V8",
        "outputId": "2b1133c2-d06b-467a-a25b-5d625b7c8ef4"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initialize the Jira connection\n",
            "Create a JiraUtils instance\n",
            "Define date intervals\n",
            "From: (2011, 8, 1) to: (2023, 10, 4), by: 120 days of distance.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.2 Retrieves all blocks of issues in range of dates"
      ],
      "metadata": {
        "id": "2fh5Gveaj-gY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fetch issues using date intervals\n",
        "print('Fetch issues using date intervals')\n",
        "block_of_issues = jira_utils.get_list_of_block_issues_by_dates(date1, date2, distance)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwF_6AZej_gs",
        "outputId": "ed03c05f-fa28-470d-93ec-cb210ba9222e"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fetch issues using date intervals\n",
            "Aguarde...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:   3%|▎         | 1/38 [00:01<01:12,  1.97s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2011/08/01\" and created<=\"2011/11/28\", qtd issues: 135\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:   5%|▌         | 2/38 [00:03<01:01,  1.70s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2011/11/29\" and created<=\"2012/03/27\", qtd issues: 105\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:   8%|▊         | 3/38 [00:04<00:55,  1.59s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2012/03/28\" and created<=\"2012/07/25\", qtd issues: 92\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  11%|█         | 4/38 [00:07<01:12,  2.13s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2012/07/26\" and created<=\"2012/11/22\", qtd issues: 212\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  13%|█▎        | 5/38 [00:10<01:20,  2.44s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2012/11/23\" and created<=\"2013/03/22\", qtd issues: 192\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  16%|█▌        | 6/38 [00:12<01:13,  2.29s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2013/03/23\" and created<=\"2013/07/20\", qtd issues: 158\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  18%|█▊        | 7/38 [00:15<01:12,  2.34s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2013/07/21\" and created<=\"2013/11/17\", qtd issues: 154\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  21%|██        | 8/38 [00:17<01:10,  2.35s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2013/11/18\" and created<=\"2014/03/17\", qtd issues: 161\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  24%|██▎       | 9/38 [00:20<01:16,  2.63s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2014/03/18\" and created<=\"2014/07/15\", qtd issues: 232\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  26%|██▋       | 10/38 [00:25<01:27,  3.14s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2014/07/16\" and created<=\"2014/11/12\", qtd issues: 227\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  29%|██▉       | 11/38 [00:28<01:25,  3.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2014/11/13\" and created<=\"2015/03/12\", qtd issues: 247\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  32%|███▏      | 12/38 [00:32<01:28,  3.40s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2015/03/13\" and created<=\"2015/07/10\", qtd issues: 310\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  34%|███▍      | 13/38 [00:38<01:44,  4.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2015/07/11\" and created<=\"2015/11/07\", qtd issues: 440\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  37%|███▋      | 14/38 [00:44<01:54,  4.76s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2015/11/08\" and created<=\"2016/03/06\", qtd issues: 568\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  39%|███▉      | 15/38 [00:51<02:03,  5.38s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2016/03/07\" and created<=\"2016/07/04\", qtd issues: 585\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  42%|████▏     | 16/38 [00:56<01:58,  5.39s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2016/07/05\" and created<=\"2016/11/01\", qtd issues: 433\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  45%|████▍     | 17/38 [01:02<01:57,  5.61s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2016/11/02\" and created<=\"2017/03/01\", qtd issues: 452\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  47%|████▋     | 18/38 [01:11<02:09,  6.48s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2017/03/02\" and created<=\"2017/06/29\", qtd issues: 703\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  50%|█████     | 19/38 [01:19<02:12,  7.00s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2017/06/30\" and created<=\"2017/10/27\", qtd issues: 591\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  53%|█████▎    | 20/38 [01:24<01:55,  6.39s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2017/10/28\" and created<=\"2018/02/24\", qtd issues: 450\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  55%|█████▌    | 21/38 [01:30<01:47,  6.31s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2018/02/25\" and created<=\"2018/06/24\", qtd issues: 501\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  58%|█████▊    | 22/38 [01:35<01:34,  5.88s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2018/06/25\" and created<=\"2018/10/22\", qtd issues: 434\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  61%|██████    | 23/38 [01:40<01:23,  5.59s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2018/10/23\" and created<=\"2019/02/19\", qtd issues: 414\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  63%|██████▎   | 24/38 [01:48<01:27,  6.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2019/02/20\" and created<=\"2019/06/19\", qtd issues: 604\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  66%|██████▌   | 25/38 [01:53<01:17,  5.96s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2019/06/20\" and created<=\"2019/10/17\", qtd issues: 486\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  68%|██████▊   | 26/38 [01:58<01:09,  5.81s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2019/10/18\" and created<=\"2020/02/14\", qtd issues: 491\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  71%|███████   | 27/38 [02:05<01:05,  5.94s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2020/02/15\" and created<=\"2020/06/13\", qtd issues: 598\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  74%|███████▎  | 28/38 [02:10<00:56,  5.61s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2020/06/14\" and created<=\"2020/10/11\", qtd issues: 439\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  76%|███████▋  | 29/38 [02:14<00:47,  5.32s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2020/10/12\" and created<=\"2021/02/08\", qtd issues: 466\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  79%|███████▉  | 30/38 [02:21<00:47,  5.89s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2021/02/09\" and created<=\"2021/06/08\", qtd issues: 597\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  82%|████████▏ | 31/38 [02:26<00:38,  5.53s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2021/06/09\" and created<=\"2021/10/06\", qtd issues: 429\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  84%|████████▍ | 32/38 [02:29<00:29,  4.87s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2021/10/07\" and created<=\"2022/02/03\", qtd issues: 283\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  87%|████████▋ | 33/38 [02:33<00:22,  4.56s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2022/02/04\" and created<=\"2022/06/03\", qtd issues: 314\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  89%|████████▉ | 34/38 [02:37<00:17,  4.35s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2022/06/04\" and created<=\"2022/10/01\", qtd issues: 313\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  92%|█████████▏| 35/38 [02:41<00:13,  4.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2022/10/02\" and created<=\"2023/01/29\", qtd issues: 386\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  95%|█████████▍| 36/38 [02:46<00:08,  4.27s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2023/01/30\" and created<=\"2023/05/29\", qtd issues: 373\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis:  97%|█████████▋| 37/38 [02:51<00:04,  4.51s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2023/05/30\" and created<=\"2023/09/26\", qtd issues: 469\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress Message Analysis: 100%|██████████| 38/38 [02:54<00:00,  4.59s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Range: project=KAFKA and created>=\"2023/09/27\" and created<=\"2024/01/24\", qtd issues: 307\n",
            "2023-11-13 21:00:32.968699\n",
            "Tempo da consulta: 0:02:54.276562\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Concatenate the block of issues into a single list\n",
        "print('Concatenate the block of issues into a single list')\n",
        "all_issues = jira_utils.concatenate_block_of_issues(block_of_issues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyJaLZg_kMUB",
        "outputId": "f2344320-2e6e-4473-8290-41c13811a5fc"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Concatenate the block of issues into a single list\n",
            "Total de issues recuperados: 14351\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#3. Quantidade total de issues analisados\n",
        "ve_qtd_total_issues = len(all_issues)\n",
        "ve_lista_com_todos_os_issues = []\n",
        "for issue in all_issues:\n",
        "  ve_lista_com_todos_os_issues.append(issue.key)"
      ],
      "metadata": {
        "id": "v3-G_0MYa5GU"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "project = 'KAFKA'\n",
        "\n",
        "# Fetch 'all_issues' from your Jira server\n",
        "\n",
        "# Call the function to analyze Jira issues for SATD\n",
        "my_jira_issues = analyze_jira_all_issues(project, all_issues)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCQ4j26Ha04B",
        "outputId": "a9dca4f2-4311-4b07-c757-0e69656b5247"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Progress jira all issues analysis: 100%|██████████| 14351/14351 [00:00<00:00, 101266.59it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.3 Filtra issues que também aparecem nos commits com arquivos críticos"
      ],
      "metadata": {
        "id": "uBKDzyYhldSi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def find_issues_id_by_project(input_string: str, project: str) -> list[str]:\n",
        "    \"\"\"Finds all Cassandra issue ID patterns in the input string.\n",
        "    Args:\n",
        "        input_string: The input string.\n",
        "        project: The pattern related to project name, for example: CASSANDRA project name\n",
        "    Returns:\n",
        "        A list of project issue IDs, if found; otherwise, an empty list.\n",
        "    \"\"\"\n",
        "    # Try to find all Cassandra issue ID patterns in the input string\n",
        "    matches = re.findall(r\"({0}-\\d+)\".format(project), input_string)\n",
        "    # Return an empty list if no matches are found\n",
        "    if not matches:\n",
        "        return []\n",
        "    # Convert the list of matches to a set to remove duplicates\n",
        "    set_matches = set(matches)\n",
        "    # Convert the set of matches back to a list\n",
        "    list_unique_matches = list(set_matches)\n",
        "\n",
        "    # Return the list of matched Cassandra issue IDs\n",
        "    return list_unique_matches"
      ],
      "metadata": {
        "id": "yoP41QSOleMh"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dict_issues_in_commits = {}\n",
        "for index in df_commits_with_satd_keywords_with_critical_files.index:\n",
        "  l_issues_in_commit = find_issues_id_by_project(input_string=df_commits_with_satd_keywords_with_critical_files.msg[index], project='KAFKA')\n",
        "  if len(l_issues_in_commit) > 0:\n",
        "    commit_hash = df_commits_with_satd_keywords_with_critical_files.commits[index]\n",
        "    dict_issues_in_commits[commit_hash] = l_issues_in_commit\n",
        "\n",
        "print(f'Existem {len(dict_issues_in_commits)} issues que foram registrados nos commits. ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qG7Tz457liPH",
        "outputId": "f3b4e4dd-1e35-4225-ad1e-dc54e70b72a3"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Existem 106 issues que foram registrados nos commits. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Relação de issues e aparecem nos commits com arquivos críticos"
      ],
      "metadata": {
        "id": "u45Bv8HJln3j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dict_issues_in_commits_aux = {}\n",
        "l_issues_in_commits, l_issues_issues_in_commits= [], []\n",
        "\n",
        "print('Commits que possuem issues registrados em suas mensagens: ')\n",
        "for k, v in dict_issues_in_commits.items():\n",
        "  l_issues_in_commits.append(k)\n",
        "  l_issues_issues_in_commits.append(v)\n",
        "\n",
        "dict_issues_in_commits_aux = {\n",
        "    'commits':l_issues_in_commits,\n",
        "    'issues_ids':l_issues_issues_in_commits\n",
        "}\n",
        "\n",
        "df_issues_in_commits_aux = pd.DataFrame(dict_issues_in_commits_aux)\n",
        "df_issues_in_commits_aux"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "WDbWU1HWlo9G",
        "outputId": "a7afc74e-a279-4b5e-9b70-e60ee956d37a"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Commits que possuem issues registrados em suas mensagens: \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      commits     issues_ids\n",
              "0    269d16d3c915d09f650ae32aa81542bd8522ca68   [KAFKA-1227]\n",
              "1    5e2a9a560d847bd0cf364d86bd6784f70d99c71a   [KAFKA-1260]\n",
              "2    c24740c7b0f6a6e7c66659da786a346650b76766   [KAFKA-1328]\n",
              "3    0699ff2ce60abb466cab5315977a224f1a70a4da   [KAFKA-1760]\n",
              "4    1c6d5bbac672cbf49591aed0889510b10e3285fa   [KAFKA-1915]\n",
              "..                                        ...            ...\n",
              "101  c9b6e19b3b37499de17da19e82b1b98e3b9f6b5c  [KAFKA-10199]\n",
              "102  06f47c3b517f5c96c1bd981369843f3483947197  [KAFKA-10199]\n",
              "103  dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd  [KAFKA-10199]\n",
              "104  896573f9bc2c6a695cb265df027c4f21214bc933  [KAFKA-14279]\n",
              "105  2fad1652942226454a44038f2350642817f9f74b  [KAFKA-10199]\n",
              "\n",
              "[106 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d50e325-343a-46b7-b4eb-b969c08689f8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>commits</th>\n",
              "      <th>issues_ids</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>269d16d3c915d09f650ae32aa81542bd8522ca68</td>\n",
              "      <td>[KAFKA-1227]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5e2a9a560d847bd0cf364d86bd6784f70d99c71a</td>\n",
              "      <td>[KAFKA-1260]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>c24740c7b0f6a6e7c66659da786a346650b76766</td>\n",
              "      <td>[KAFKA-1328]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0699ff2ce60abb466cab5315977a224f1a70a4da</td>\n",
              "      <td>[KAFKA-1760]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1c6d5bbac672cbf49591aed0889510b10e3285fa</td>\n",
              "      <td>[KAFKA-1915]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>c9b6e19b3b37499de17da19e82b1b98e3b9f6b5c</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>06f47c3b517f5c96c1bd981369843f3483947197</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>896573f9bc2c6a695cb265df027c4f21214bc933</td>\n",
              "      <td>[KAFKA-14279]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>2fad1652942226454a44038f2350642817f9f74b</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>106 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d50e325-343a-46b7-b4eb-b969c08689f8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5d50e325-343a-46b7-b4eb-b969c08689f8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5d50e325-343a-46b7-b4eb-b969c08689f8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8c9b7cbe-2b38-4fec-9f3a-9a9dc946d506\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8c9b7cbe-2b38-4fec-9f3a-9a9dc946d506')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8c9b7cbe-2b38-4fec-9f3a-9a9dc946d506 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lista_issues_in_commits_with_critical_files = df_issues_in_commits_aux.commits.to_list()\n",
        "print(lista_issues_in_commits_with_critical_files)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2roErLZnX13T",
        "outputId": "d20510bb-ab20-4405-826e-d7367cc9b010"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['269d16d3c915d09f650ae32aa81542bd8522ca68', '5e2a9a560d847bd0cf364d86bd6784f70d99c71a', 'c24740c7b0f6a6e7c66659da786a346650b76766', '0699ff2ce60abb466cab5315977a224f1a70a4da', '1c6d5bbac672cbf49591aed0889510b10e3285fa', '0b92cec1e07a1f2d9aa70f3ecd7d0fb12290d2e2', '01f20e029fbd068a8493492a2c4a1364a5668579', 'd22987f01d50549d855ae092b69f520d75bfeb7b', '99c0686be2141a0fffe1c55e279370a87ef8c1ea', '9dbeb71ab258955e04b46991c1baf880b07633f4', '48b4d6938dfb9dc72c941e979ff0d0c4d921f743', '5e769ccd5bf81f82c90069d4b0e79182a1cfe3c0', '86eb74d9236c586af5889fe79f4b9e066c9c2af3', '1b5687b9e4cd10e0f91fb921d3569fdd44be163d', '7c33475274cb6e65a8e8d907e7fef6e56bc8c8e6', 'e0098b4567541d1c0cefc6f57ce38f67a9133b5e', '45e7f71309f9a3e30d25a6ddd3171c67e3e79286', '39c3512eceedebcb6e50f8c6c4ef66601ff7dbc4', 'd05fa0a03bc9bcfcff8d73cbf1b22832ebdb75a2', '40d731b8712950122915795acca43886851a73b6', 'a62eb5993f5517a64dd1020b0a9bbd1012f7ee67', '959cf09e8653f4b8255f49c6f4c258ed1a5ec38e', '5ae97196ae149e58f6cfa3c5b6d968cbd7cb6787', '57da044a991ebf8913d44dfcfa6a27729f54a4d5', '878b78acb68d0fa62eb3c8f21d572664ca2bcdd0', '845c6eae1f6c6bcf117f5baa53bb19b4611c0528', '30e78fa00650b258f3ab5ef6c9bdf5ca137289c0', 'dea0719e99211684775780f5da8b93835d7a5dac', '3a58407e2e0aee0bb2c5e343fbe98c7f28fc6f3e', 'fb42558e2500835722a4e5028896ddae4f407d6f', 'bb629f2243c4462db2a863793c190d734f11f3c6', 'be36b322749003581474e2c84a3ec9ba2aaec53c', 'ae237be1bbfd7d1b84a5095491a6131cd3cc9346', '3b4c347949c02b1e2b1dd473deda0f8d2304d027', '6f7ed15dad8d914ae65a595eef327c0510f11469', '4b71c0bdc1acf244e3c96fa809a1a0e48471d586', '3400d0c3cc07d5d3136bf9a19142b36cca93f92d', 'ca0c071c108c9fd31a759e1cd1c4f89bdc5ac47e', '5bd06f1d542e6b588a1d402d059bc24690017d32', 'bdf4cba047334943aa8357585c4fb379b27e9ffd', 'd0e7c6b9304b23ced046934c799df0cba39c28e5', 'f69d94158ce72f4ef2df5b98f2395c2b2e61251e', 'e71dce89c0da50f3eccc47d0fc050c92d5a99b88', '1110f66fa7278212160c6e8392b6e6ab09560452', '5d798511b12c5ef7555e4234fdd99a360176e435', '1844bf2b2f4cdf5a8209d7ceccb6701fc7dcf768', 'da2205578be3228ce40eb5e59f6bbcb0c8da0aba', '125d69caee993710bc07523fdd3deb0e06b10636', 'eaabb6cd0173c4f6854eb5da39194a7e3fc0162c', 'e3c32391f95d82a217295c7e4c1519981124bc3f', '5df1eee7d689e18ac2f7b74410e7a30159d3afdc', '043951753b6fb6c8bae6d25d7a6a97e74b614cac', '82c6d429e70f1c782103953072dc6dbec650dd6e', 'b1aa1912f08765d9914e3d036deee6b71ea009dd', '394aa7426117d0d04666c1c2a63d5f98229b7894', '1b170df31c7304f6d4c938b5e0c2a09ae1e9189d', '2d8049b713f2b9982bd43e7340a0e0f302f30d6b', '1e207b2ef83db4296fb944ade4c301794f9e8460', '837f31dd1850b179918f83338b4b4487486b2c58', 'd98ec33364d4ddb6ee53e0cd6001857207ba2089', '1354371d4f4dd208458c776e1f27715ec5f77f47', '3c14feb442219f58e0008a6242a24f1a492ca337', '0d778987ee3b920b5c876696385c0792671adef8', '283a19481d1ce4a77f5f465e7b96288db22a8ff1', '9d544212e69269f155bb3a51f94a9b13cf1fa565', '58d6043d4c88892f6718e31799503c3482000ea2', 'e867a58425876767b952e06892c72b5e13066acc', '83c7c0158fc62c294267f0ae60e9f52945e6a678', '4090f9a2b0a95e4da127e4786007542276d97520', 'd8756e81c57040130ec163e63798663d62298589', '3b6573c150527efb3e90b2b3d1a059ac5c2db80b', '97d107a270d3e9b795b42d209f838f2f81009ce8', '89cd2f2a0b21368297323437fd15ba6341e4707b', '1ad5f346cb88072c419142c86c3d897aad2de996', '353aa6206d43b4923d21510797709bbbb210f6b6', '58f7a973149f66b980f99c3f2bd688d70b06b2eb', '27824baa210b9744074ac1fcdabbf1c8ff1b42ca', '8e083e1b8879e732522bb9a560c82ff84b176cc9', '2239004907b29e00811fee9ded5a790172701a03', 'b351493543b7e26aa345df3b568d0dc08a8c8d91', '7915d5e5f826a71c11e1c9183c84702410f7209a', 'aef6cd6e9995b42db2cefa7d715321d0edee5628', 'cb3dc6785bc8263d8e1fc695e499e1eeb9df52a4', '192db666c913c3fb2bfe3568ac51aa4ddb010a25', 'd2cb2dc45d536ae124e3da25d6d5a4e932a23a27', 'fb2eef94a4ae91501d869e2075c511b433877099', '48379bd6e5075fc73449e235f1b527114f5a430c', '2b8aff58b575c199ee8372e5689420c9d77357a5', '4710a491463a91ec12c670ea50c139fc14134e80', '6854eb8332d6ef1f1c6216d2f67d6e146b1ef60f', '908a6d2ad7807642b917231769c8d3b6a0c1ad16', '14c24490505090b0bedeabd891542915692501be', '265d3199ec741928c1731397302f6c320cf6af40', '14c6030c6ad70997863070f5b93c817ea8829425', 'cd4a1cb4101abcc7cdd1d2d0d73662114108f3e5', '6eb57f6df1706c6ec228123cea1c120bd53b62d6', '539f006e65d9060cd46a4052d1b70f2312d8ca34', 'b68463c250ec99d86ffacdbd45e58059d0ca51e1', '0924fd3f9f75c446310ed1e97b44bbc3f33c6c31', 'fa0324485b1e76388bb15ea98170cfb9202aab1f', 'cc2aa96ae4063bdc50d692eaf5d11becbb94154b', 'c9b6e19b3b37499de17da19e82b1b98e3b9f6b5c', '06f47c3b517f5c96c1bd981369843f3483947197', 'dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd', '896573f9bc2c6a695cb265df027c4f21214bc933', '2fad1652942226454a44038f2350642817f9f74b']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_commits_with_satd_keywords_with_critical_files_with_issues = df_commits_with_satd_keywords_with_critical_files[df_commits_with_satd_keywords_with_critical_files.commits.isin(lista_issues_in_commits_with_critical_files)]\n",
        "df_commits_with_satd_keywords_with_critical_files_with_issues"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nmflXVDqYSpF",
        "outputId": "da4685c5-1eb6-4bd1-9da7-cde0dce08cc8"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      commits       data  \\\n",
              "0    269d16d3c915d09f650ae32aa81542bd8522ca68  28/1/2014   \n",
              "1    5e2a9a560d847bd0cf364d86bd6784f70d99c71a  27/2/2014   \n",
              "2    c24740c7b0f6a6e7c66659da786a346650b76766  20/5/2014   \n",
              "3    0699ff2ce60abb466cab5315977a224f1a70a4da  29/1/2015   \n",
              "4    1c6d5bbac672cbf49591aed0889510b10e3285fa   3/2/2015   \n",
              "..                                        ...        ...   \n",
              "119  c9b6e19b3b37499de17da19e82b1b98e3b9f6b5c  21/7/2022   \n",
              "120  06f47c3b517f5c96c1bd981369843f3483947197  27/7/2022   \n",
              "121  dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd  15/8/2022   \n",
              "122  896573f9bc2c6a695cb265df027c4f21214bc933   9/1/2023   \n",
              "123  2fad1652942226454a44038f2350642817f9f74b  24/2/2023   \n",
              "\n",
              "                                                   msg  lines  files  \\\n",
              "0                             KAFKA-1227 New producer!  11720    121   \n",
              "1    KAFKA-1260 Integration Test for New Producer P...    507     14   \n",
              "2    KAFKA-1328 New consumer APIs; reviewed by Jun ...   1744     11   \n",
              "3                            KAFKA-1760: New consumer.   5931     89   \n",
              "4            KAFKA-1915: Add checkstyle for java code.   5440     97   \n",
              "..                                                 ...    ...    ...   \n",
              "119  KAFKA-10199: Cleanup TaskManager and Task inte...    903     16   \n",
              "120  KAFKA-10199: Further refactor task lifecycle m...   1065     18   \n",
              "121  KAFKA-10199: Handle task closure and recycling...    667     11   \n",
              "122  KAFKA-14279: Add 3.3.x streams system tests (#...   1365     14   \n",
              "123  KAFKA-10199: Add task updater metrics, part 1 ...    509      9   \n",
              "\n",
              "                               critical_files  \\\n",
              "0                        [KafkaProducer.java]   \n",
              "1                        [KafkaProducer.java]   \n",
              "2    [KafkaConsumer.java, KafkaProducer.java]   \n",
              "3    [KafkaConsumer.java, KafkaProducer.java]   \n",
              "4    [KafkaConsumer.java, KafkaProducer.java]   \n",
              "..                                        ...   \n",
              "119      [StreamTask.java, StreamThread.java]   \n",
              "120                         [StreamTask.java]   \n",
              "121                         [StreamTask.java]   \n",
              "122                      [StreamsConfig.java]   \n",
              "123                       [StreamThread.java]   \n",
              "\n",
              "                                       keywords  \\\n",
              "0                [TODO, TODO, TODO, TODO, TODO]   \n",
              "1    [TODO, TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "2          [TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "3                [TODO, TODO, TODO, TODO, TODO]   \n",
              "4          [TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "..                                          ...   \n",
              "119                          [TODO, TODO, TODO]   \n",
              "120                          [TODO, TODO, TODO]   \n",
              "121              [TODO, TODO, TODO, TODO, TODO]   \n",
              "122                                      [TODO]   \n",
              "123                          [TODO, TODO, TODO]   \n",
              "\n",
              "                                              comments  \\\n",
              "0    [        /* TODO: add docs */,                ...   \n",
              "1    [    // TODO: errorCode 4 for InvalidFetchSize...   \n",
              "2    [        /* TODO: add config docs */,         ...   \n",
              "3    [            // TODO: list offset call could b...   \n",
              "4    [        // TODO read uncompressed content siz...   \n",
              "..                                                 ...   \n",
              "119  [     * TODO: we should be able to not need th...   \n",
              "120  [     * TODO: we pass in the new input partiti...   \n",
              "121  [    // TODO: convert to StreamTask when we re...   \n",
              "122  [            // However, values are also use a...   \n",
              "123  [            // TODO (?) If we cannot fetch re...   \n",
              "\n",
              "                                           diff_commit  \\\n",
              "0    [{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...   \n",
              "1    [{'KafkaProducer.java': '@@ -33,6 +33,7 @@ imp...   \n",
              "2    [{'KafkaConsumer.java': '@@ -0,0 +1,575 @@\n",
              "+/*...   \n",
              "3    [{'KafkaConsumer.java': '@@ -9,380 +9,447 @@\n",
              " ...   \n",
              "4    [{'KafkaConsumer.java': '@@ -30,13 +30,13 @@ i...   \n",
              "..                                                 ...   \n",
              "119  [{'StreamTask.java': '@@ -127,7 +127,7 @@ publ...   \n",
              "120  [{'StreamTask.java': '@@ -83,7 +83,6 @@ public...   \n",
              "121  [{'StreamTask.java': '@@ -568,7 +568,7 @@ publ...   \n",
              "122  [{'StreamsConfig.java': '@@ -392,6 +392,12 @@ ...   \n",
              "123  [{'StreamThread.java': '@@ -404,7 +404,7 @@ pu...   \n",
              "\n",
              "                                        modified_files  \\\n",
              "0    [build.sbt, BufferExhaustedException.java, Cal...   \n",
              "1    [KafkaProducer.java, MockProducer.java, Record...   \n",
              "2    [Consumer.java, ConsumerConfig.java, ConsumerR...   \n",
              "3    [build.gradle, ClientRequest.java, ClusterConn...   \n",
              "4    [README.md, build.gradle, checkstyle.xml, impo...   \n",
              "..                                                 ...   \n",
              "119  [AbstractTask.java, ActiveTaskCreator.java, Pa...   \n",
              "120  [AbstractTask.java, ActiveTaskCreator.java, St...   \n",
              "121  [ActiveTaskCreator.java, StandbyTask.java, Sta...   \n",
              "122  [build.gradle, dependencies.gradle, settings.g...   \n",
              "123  [ChangelogReader.java, DefaultStateUpdater.jav...   \n",
              "\n",
              "                                   dict_modified_files  \n",
              "0    [@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...  \n",
              "1    [@@ -33,6 +33,7 @@ import org.apache.kafka.com...  \n",
              "2    [@@ -0,0 +1,125 @@\\n+/**\\n+ * Licensed to the ...  \n",
              "3    [@@ -369,6 +369,7 @@ project(':clients') {\\n  ...  \n",
              "4    [@@ -98,6 +98,9 @@ Please note for this to wor...  \n",
              "..                                                 ...  \n",
              "119  [@@ -20,6 +20,7 @@ import org.apache.kafka.com...  \n",
              "120  [@@ -43,10 +43,11 @@ public abstract class Abs...  \n",
              "121  [@@ -135,6 +135,7 @@ class ActiveTaskCreator {...  \n",
              "122  [@@ -2040,6 +2040,7 @@ project(':streams') {\\n...  \n",
              "123  [@@ -28,8 +28,10 @@ import java.util.Set;\\n pu...  \n",
              "\n",
              "[106 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29835a13-6e82-4c30-b8ad-7da896dd03c6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>commits</th>\n",
              "      <th>data</th>\n",
              "      <th>msg</th>\n",
              "      <th>lines</th>\n",
              "      <th>files</th>\n",
              "      <th>critical_files</th>\n",
              "      <th>keywords</th>\n",
              "      <th>comments</th>\n",
              "      <th>diff_commit</th>\n",
              "      <th>modified_files</th>\n",
              "      <th>dict_modified_files</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>269d16d3c915d09f650ae32aa81542bd8522ca68</td>\n",
              "      <td>28/1/2014</td>\n",
              "      <td>KAFKA-1227 New producer!</td>\n",
              "      <td>11720</td>\n",
              "      <td>121</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        /* TODO: add docs */,                ...</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...</td>\n",
              "      <td>[build.sbt, BufferExhaustedException.java, Cal...</td>\n",
              "      <td>[@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5e2a9a560d847bd0cf364d86bd6784f70d99c71a</td>\n",
              "      <td>27/2/2014</td>\n",
              "      <td>KAFKA-1260 Integration Test for New Producer P...</td>\n",
              "      <td>507</td>\n",
              "      <td>14</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[    // TODO: errorCode 4 for InvalidFetchSize...</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -33,6 +33,7 @@ imp...</td>\n",
              "      <td>[KafkaProducer.java, MockProducer.java, Record...</td>\n",
              "      <td>[@@ -33,6 +33,7 @@ import org.apache.kafka.com...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>c24740c7b0f6a6e7c66659da786a346650b76766</td>\n",
              "      <td>20/5/2014</td>\n",
              "      <td>KAFKA-1328 New consumer APIs; reviewed by Jun ...</td>\n",
              "      <td>1744</td>\n",
              "      <td>11</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        /* TODO: add config docs */,         ...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -0,0 +1,575 @@\n",
              "+/*...</td>\n",
              "      <td>[Consumer.java, ConsumerConfig.java, ConsumerR...</td>\n",
              "      <td>[@@ -0,0 +1,125 @@\\n+/**\\n+ * Licensed to the ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0699ff2ce60abb466cab5315977a224f1a70a4da</td>\n",
              "      <td>29/1/2015</td>\n",
              "      <td>KAFKA-1760: New consumer.</td>\n",
              "      <td>5931</td>\n",
              "      <td>89</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[            // TODO: list offset call could b...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -9,380 +9,447 @@\n",
              " ...</td>\n",
              "      <td>[build.gradle, ClientRequest.java, ClusterConn...</td>\n",
              "      <td>[@@ -369,6 +369,7 @@ project(':clients') {\\n  ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1c6d5bbac672cbf49591aed0889510b10e3285fa</td>\n",
              "      <td>3/2/2015</td>\n",
              "      <td>KAFKA-1915: Add checkstyle for java code.</td>\n",
              "      <td>5440</td>\n",
              "      <td>97</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        // TODO read uncompressed content siz...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -30,13 +30,13 @@ i...</td>\n",
              "      <td>[README.md, build.gradle, checkstyle.xml, impo...</td>\n",
              "      <td>[@@ -98,6 +98,9 @@ Please note for this to wor...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>c9b6e19b3b37499de17da19e82b1b98e3b9f6b5c</td>\n",
              "      <td>21/7/2022</td>\n",
              "      <td>KAFKA-10199: Cleanup TaskManager and Task inte...</td>\n",
              "      <td>903</td>\n",
              "      <td>16</td>\n",
              "      <td>[StreamTask.java, StreamThread.java]</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[     * TODO: we should be able to not need th...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -127,7 +127,7 @@ publ...</td>\n",
              "      <td>[AbstractTask.java, ActiveTaskCreator.java, Pa...</td>\n",
              "      <td>[@@ -20,6 +20,7 @@ import org.apache.kafka.com...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>06f47c3b517f5c96c1bd981369843f3483947197</td>\n",
              "      <td>27/7/2022</td>\n",
              "      <td>KAFKA-10199: Further refactor task lifecycle m...</td>\n",
              "      <td>1065</td>\n",
              "      <td>18</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[     * TODO: we pass in the new input partiti...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -83,7 +83,6 @@ public...</td>\n",
              "      <td>[AbstractTask.java, ActiveTaskCreator.java, St...</td>\n",
              "      <td>[@@ -43,10 +43,11 @@ public abstract class Abs...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd</td>\n",
              "      <td>15/8/2022</td>\n",
              "      <td>KAFKA-10199: Handle task closure and recycling...</td>\n",
              "      <td>667</td>\n",
              "      <td>11</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[    // TODO: convert to StreamTask when we re...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -568,7 +568,7 @@ publ...</td>\n",
              "      <td>[ActiveTaskCreator.java, StandbyTask.java, Sta...</td>\n",
              "      <td>[@@ -135,6 +135,7 @@ class ActiveTaskCreator {...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>896573f9bc2c6a695cb265df027c4f21214bc933</td>\n",
              "      <td>9/1/2023</td>\n",
              "      <td>KAFKA-14279: Add 3.3.x streams system tests (#...</td>\n",
              "      <td>1365</td>\n",
              "      <td>14</td>\n",
              "      <td>[StreamsConfig.java]</td>\n",
              "      <td>[TODO]</td>\n",
              "      <td>[            // However, values are also use a...</td>\n",
              "      <td>[{'StreamsConfig.java': '@@ -392,6 +392,12 @@ ...</td>\n",
              "      <td>[build.gradle, dependencies.gradle, settings.g...</td>\n",
              "      <td>[@@ -2040,6 +2040,7 @@ project(':streams') {\\n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>2fad1652942226454a44038f2350642817f9f74b</td>\n",
              "      <td>24/2/2023</td>\n",
              "      <td>KAFKA-10199: Add task updater metrics, part 1 ...</td>\n",
              "      <td>509</td>\n",
              "      <td>9</td>\n",
              "      <td>[StreamThread.java]</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[            // TODO (?) If we cannot fetch re...</td>\n",
              "      <td>[{'StreamThread.java': '@@ -404,7 +404,7 @@ pu...</td>\n",
              "      <td>[ChangelogReader.java, DefaultStateUpdater.jav...</td>\n",
              "      <td>[@@ -28,8 +28,10 @@ import java.util.Set;\\n pu...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>106 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29835a13-6e82-4c30-b8ad-7da896dd03c6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-29835a13-6e82-4c30-b8ad-7da896dd03c6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-29835a13-6e82-4c30-b8ad-7da896dd03c6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-327e04ac-1b34-4f9e-9e7a-96f05db66d3b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-327e04ac-1b34-4f9e-9e7a-96f05db66d3b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-327e04ac-1b34-4f9e-9e7a-96f05db66d3b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_issues_in_commits_with_critical_classes_kl_satd = pd.merge(df_commits_with_satd_keywords_with_critical_files_with_issues, df_issues_in_commits_aux, how='inner')\n",
        "df_issues_in_commits_with_critical_classes_kl_satd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "54hJvQ-CZgo5",
        "outputId": "cd931120-5bc6-4463-b912-204ee9acff41"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      commits       data  \\\n",
              "0    269d16d3c915d09f650ae32aa81542bd8522ca68  28/1/2014   \n",
              "1    5e2a9a560d847bd0cf364d86bd6784f70d99c71a  27/2/2014   \n",
              "2    c24740c7b0f6a6e7c66659da786a346650b76766  20/5/2014   \n",
              "3    0699ff2ce60abb466cab5315977a224f1a70a4da  29/1/2015   \n",
              "4    1c6d5bbac672cbf49591aed0889510b10e3285fa   3/2/2015   \n",
              "..                                        ...        ...   \n",
              "101  c9b6e19b3b37499de17da19e82b1b98e3b9f6b5c  21/7/2022   \n",
              "102  06f47c3b517f5c96c1bd981369843f3483947197  27/7/2022   \n",
              "103  dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd  15/8/2022   \n",
              "104  896573f9bc2c6a695cb265df027c4f21214bc933   9/1/2023   \n",
              "105  2fad1652942226454a44038f2350642817f9f74b  24/2/2023   \n",
              "\n",
              "                                                   msg  lines  files  \\\n",
              "0                             KAFKA-1227 New producer!  11720    121   \n",
              "1    KAFKA-1260 Integration Test for New Producer P...    507     14   \n",
              "2    KAFKA-1328 New consumer APIs; reviewed by Jun ...   1744     11   \n",
              "3                            KAFKA-1760: New consumer.   5931     89   \n",
              "4            KAFKA-1915: Add checkstyle for java code.   5440     97   \n",
              "..                                                 ...    ...    ...   \n",
              "101  KAFKA-10199: Cleanup TaskManager and Task inte...    903     16   \n",
              "102  KAFKA-10199: Further refactor task lifecycle m...   1065     18   \n",
              "103  KAFKA-10199: Handle task closure and recycling...    667     11   \n",
              "104  KAFKA-14279: Add 3.3.x streams system tests (#...   1365     14   \n",
              "105  KAFKA-10199: Add task updater metrics, part 1 ...    509      9   \n",
              "\n",
              "                               critical_files  \\\n",
              "0                        [KafkaProducer.java]   \n",
              "1                        [KafkaProducer.java]   \n",
              "2    [KafkaConsumer.java, KafkaProducer.java]   \n",
              "3    [KafkaConsumer.java, KafkaProducer.java]   \n",
              "4    [KafkaConsumer.java, KafkaProducer.java]   \n",
              "..                                        ...   \n",
              "101      [StreamTask.java, StreamThread.java]   \n",
              "102                         [StreamTask.java]   \n",
              "103                         [StreamTask.java]   \n",
              "104                      [StreamsConfig.java]   \n",
              "105                       [StreamThread.java]   \n",
              "\n",
              "                                       keywords  \\\n",
              "0                [TODO, TODO, TODO, TODO, TODO]   \n",
              "1    [TODO, TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "2          [TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "3                [TODO, TODO, TODO, TODO, TODO]   \n",
              "4          [TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "..                                          ...   \n",
              "101                          [TODO, TODO, TODO]   \n",
              "102                          [TODO, TODO, TODO]   \n",
              "103              [TODO, TODO, TODO, TODO, TODO]   \n",
              "104                                      [TODO]   \n",
              "105                          [TODO, TODO, TODO]   \n",
              "\n",
              "                                              comments  \\\n",
              "0    [        /* TODO: add docs */,                ...   \n",
              "1    [    // TODO: errorCode 4 for InvalidFetchSize...   \n",
              "2    [        /* TODO: add config docs */,         ...   \n",
              "3    [            // TODO: list offset call could b...   \n",
              "4    [        // TODO read uncompressed content siz...   \n",
              "..                                                 ...   \n",
              "101  [     * TODO: we should be able to not need th...   \n",
              "102  [     * TODO: we pass in the new input partiti...   \n",
              "103  [    // TODO: convert to StreamTask when we re...   \n",
              "104  [            // However, values are also use a...   \n",
              "105  [            // TODO (?) If we cannot fetch re...   \n",
              "\n",
              "                                           diff_commit  \\\n",
              "0    [{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...   \n",
              "1    [{'KafkaProducer.java': '@@ -33,6 +33,7 @@ imp...   \n",
              "2    [{'KafkaConsumer.java': '@@ -0,0 +1,575 @@\n",
              "+/*...   \n",
              "3    [{'KafkaConsumer.java': '@@ -9,380 +9,447 @@\n",
              " ...   \n",
              "4    [{'KafkaConsumer.java': '@@ -30,13 +30,13 @@ i...   \n",
              "..                                                 ...   \n",
              "101  [{'StreamTask.java': '@@ -127,7 +127,7 @@ publ...   \n",
              "102  [{'StreamTask.java': '@@ -83,7 +83,6 @@ public...   \n",
              "103  [{'StreamTask.java': '@@ -568,7 +568,7 @@ publ...   \n",
              "104  [{'StreamsConfig.java': '@@ -392,6 +392,12 @@ ...   \n",
              "105  [{'StreamThread.java': '@@ -404,7 +404,7 @@ pu...   \n",
              "\n",
              "                                        modified_files  \\\n",
              "0    [build.sbt, BufferExhaustedException.java, Cal...   \n",
              "1    [KafkaProducer.java, MockProducer.java, Record...   \n",
              "2    [Consumer.java, ConsumerConfig.java, ConsumerR...   \n",
              "3    [build.gradle, ClientRequest.java, ClusterConn...   \n",
              "4    [README.md, build.gradle, checkstyle.xml, impo...   \n",
              "..                                                 ...   \n",
              "101  [AbstractTask.java, ActiveTaskCreator.java, Pa...   \n",
              "102  [AbstractTask.java, ActiveTaskCreator.java, St...   \n",
              "103  [ActiveTaskCreator.java, StandbyTask.java, Sta...   \n",
              "104  [build.gradle, dependencies.gradle, settings.g...   \n",
              "105  [ChangelogReader.java, DefaultStateUpdater.jav...   \n",
              "\n",
              "                                   dict_modified_files     issues_ids  \n",
              "0    [@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...   [KAFKA-1227]  \n",
              "1    [@@ -33,6 +33,7 @@ import org.apache.kafka.com...   [KAFKA-1260]  \n",
              "2    [@@ -0,0 +1,125 @@\\n+/**\\n+ * Licensed to the ...   [KAFKA-1328]  \n",
              "3    [@@ -369,6 +369,7 @@ project(':clients') {\\n  ...   [KAFKA-1760]  \n",
              "4    [@@ -98,6 +98,9 @@ Please note for this to wor...   [KAFKA-1915]  \n",
              "..                                                 ...            ...  \n",
              "101  [@@ -20,6 +20,7 @@ import org.apache.kafka.com...  [KAFKA-10199]  \n",
              "102  [@@ -43,10 +43,11 @@ public abstract class Abs...  [KAFKA-10199]  \n",
              "103  [@@ -135,6 +135,7 @@ class ActiveTaskCreator {...  [KAFKA-10199]  \n",
              "104  [@@ -2040,6 +2040,7 @@ project(':streams') {\\n...  [KAFKA-14279]  \n",
              "105  [@@ -28,8 +28,10 @@ import java.util.Set;\\n pu...  [KAFKA-10199]  \n",
              "\n",
              "[106 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-020bb141-bc17-48f1-a4fd-bda16174805c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>commits</th>\n",
              "      <th>data</th>\n",
              "      <th>msg</th>\n",
              "      <th>lines</th>\n",
              "      <th>files</th>\n",
              "      <th>critical_files</th>\n",
              "      <th>keywords</th>\n",
              "      <th>comments</th>\n",
              "      <th>diff_commit</th>\n",
              "      <th>modified_files</th>\n",
              "      <th>dict_modified_files</th>\n",
              "      <th>issues_ids</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>269d16d3c915d09f650ae32aa81542bd8522ca68</td>\n",
              "      <td>28/1/2014</td>\n",
              "      <td>KAFKA-1227 New producer!</td>\n",
              "      <td>11720</td>\n",
              "      <td>121</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        /* TODO: add docs */,                ...</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...</td>\n",
              "      <td>[build.sbt, BufferExhaustedException.java, Cal...</td>\n",
              "      <td>[@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...</td>\n",
              "      <td>[KAFKA-1227]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5e2a9a560d847bd0cf364d86bd6784f70d99c71a</td>\n",
              "      <td>27/2/2014</td>\n",
              "      <td>KAFKA-1260 Integration Test for New Producer P...</td>\n",
              "      <td>507</td>\n",
              "      <td>14</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[    // TODO: errorCode 4 for InvalidFetchSize...</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -33,6 +33,7 @@ imp...</td>\n",
              "      <td>[KafkaProducer.java, MockProducer.java, Record...</td>\n",
              "      <td>[@@ -33,6 +33,7 @@ import org.apache.kafka.com...</td>\n",
              "      <td>[KAFKA-1260]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>c24740c7b0f6a6e7c66659da786a346650b76766</td>\n",
              "      <td>20/5/2014</td>\n",
              "      <td>KAFKA-1328 New consumer APIs; reviewed by Jun ...</td>\n",
              "      <td>1744</td>\n",
              "      <td>11</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        /* TODO: add config docs */,         ...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -0,0 +1,575 @@\n",
              "+/*...</td>\n",
              "      <td>[Consumer.java, ConsumerConfig.java, ConsumerR...</td>\n",
              "      <td>[@@ -0,0 +1,125 @@\\n+/**\\n+ * Licensed to the ...</td>\n",
              "      <td>[KAFKA-1328]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0699ff2ce60abb466cab5315977a224f1a70a4da</td>\n",
              "      <td>29/1/2015</td>\n",
              "      <td>KAFKA-1760: New consumer.</td>\n",
              "      <td>5931</td>\n",
              "      <td>89</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[            // TODO: list offset call could b...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -9,380 +9,447 @@\n",
              " ...</td>\n",
              "      <td>[build.gradle, ClientRequest.java, ClusterConn...</td>\n",
              "      <td>[@@ -369,6 +369,7 @@ project(':clients') {\\n  ...</td>\n",
              "      <td>[KAFKA-1760]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1c6d5bbac672cbf49591aed0889510b10e3285fa</td>\n",
              "      <td>3/2/2015</td>\n",
              "      <td>KAFKA-1915: Add checkstyle for java code.</td>\n",
              "      <td>5440</td>\n",
              "      <td>97</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        // TODO read uncompressed content siz...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -30,13 +30,13 @@ i...</td>\n",
              "      <td>[README.md, build.gradle, checkstyle.xml, impo...</td>\n",
              "      <td>[@@ -98,6 +98,9 @@ Please note for this to wor...</td>\n",
              "      <td>[KAFKA-1915]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>c9b6e19b3b37499de17da19e82b1b98e3b9f6b5c</td>\n",
              "      <td>21/7/2022</td>\n",
              "      <td>KAFKA-10199: Cleanup TaskManager and Task inte...</td>\n",
              "      <td>903</td>\n",
              "      <td>16</td>\n",
              "      <td>[StreamTask.java, StreamThread.java]</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[     * TODO: we should be able to not need th...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -127,7 +127,7 @@ publ...</td>\n",
              "      <td>[AbstractTask.java, ActiveTaskCreator.java, Pa...</td>\n",
              "      <td>[@@ -20,6 +20,7 @@ import org.apache.kafka.com...</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>06f47c3b517f5c96c1bd981369843f3483947197</td>\n",
              "      <td>27/7/2022</td>\n",
              "      <td>KAFKA-10199: Further refactor task lifecycle m...</td>\n",
              "      <td>1065</td>\n",
              "      <td>18</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[     * TODO: we pass in the new input partiti...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -83,7 +83,6 @@ public...</td>\n",
              "      <td>[AbstractTask.java, ActiveTaskCreator.java, St...</td>\n",
              "      <td>[@@ -43,10 +43,11 @@ public abstract class Abs...</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd</td>\n",
              "      <td>15/8/2022</td>\n",
              "      <td>KAFKA-10199: Handle task closure and recycling...</td>\n",
              "      <td>667</td>\n",
              "      <td>11</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[    // TODO: convert to StreamTask when we re...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -568,7 +568,7 @@ publ...</td>\n",
              "      <td>[ActiveTaskCreator.java, StandbyTask.java, Sta...</td>\n",
              "      <td>[@@ -135,6 +135,7 @@ class ActiveTaskCreator {...</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>896573f9bc2c6a695cb265df027c4f21214bc933</td>\n",
              "      <td>9/1/2023</td>\n",
              "      <td>KAFKA-14279: Add 3.3.x streams system tests (#...</td>\n",
              "      <td>1365</td>\n",
              "      <td>14</td>\n",
              "      <td>[StreamsConfig.java]</td>\n",
              "      <td>[TODO]</td>\n",
              "      <td>[            // However, values are also use a...</td>\n",
              "      <td>[{'StreamsConfig.java': '@@ -392,6 +392,12 @@ ...</td>\n",
              "      <td>[build.gradle, dependencies.gradle, settings.g...</td>\n",
              "      <td>[@@ -2040,6 +2040,7 @@ project(':streams') {\\n...</td>\n",
              "      <td>[KAFKA-14279]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>2fad1652942226454a44038f2350642817f9f74b</td>\n",
              "      <td>24/2/2023</td>\n",
              "      <td>KAFKA-10199: Add task updater metrics, part 1 ...</td>\n",
              "      <td>509</td>\n",
              "      <td>9</td>\n",
              "      <td>[StreamThread.java]</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[            // TODO (?) If we cannot fetch re...</td>\n",
              "      <td>[{'StreamThread.java': '@@ -404,7 +404,7 @@ pu...</td>\n",
              "      <td>[ChangelogReader.java, DefaultStateUpdater.jav...</td>\n",
              "      <td>[@@ -28,8 +28,10 @@ import java.util.Set;\\n pu...</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>106 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-020bb141-bc17-48f1-a4fd-bda16174805c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-020bb141-bc17-48f1-a4fd-bda16174805c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-020bb141-bc17-48f1-a4fd-bda16174805c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bf4b91a8-ff1c-4fe8-b261-ebffb40e7052\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bf4b91a8-ff1c-4fe8-b261-ebffb40e7052')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bf4b91a8-ff1c-4fe8-b261-ebffb40e7052 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_columns = ['commits', 'issues_ids', 'msg','data', 'lines', 'files', 'critical_files',\n",
        "       'keywords', 'comments', 'diff_commit', 'modified_files',\n",
        "       'dict_modified_files']\n",
        "df_issues_in_commits_with_critical_classes_kl_satd[my_columns]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0KIqLivsl-Q8",
        "outputId": "e2e8249c-cda0-4d0d-b2dd-bc93c8b9d4f8"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      commits     issues_ids  \\\n",
              "0    269d16d3c915d09f650ae32aa81542bd8522ca68   [KAFKA-1227]   \n",
              "1    5e2a9a560d847bd0cf364d86bd6784f70d99c71a   [KAFKA-1260]   \n",
              "2    c24740c7b0f6a6e7c66659da786a346650b76766   [KAFKA-1328]   \n",
              "3    0699ff2ce60abb466cab5315977a224f1a70a4da   [KAFKA-1760]   \n",
              "4    1c6d5bbac672cbf49591aed0889510b10e3285fa   [KAFKA-1915]   \n",
              "..                                        ...            ...   \n",
              "101  c9b6e19b3b37499de17da19e82b1b98e3b9f6b5c  [KAFKA-10199]   \n",
              "102  06f47c3b517f5c96c1bd981369843f3483947197  [KAFKA-10199]   \n",
              "103  dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd  [KAFKA-10199]   \n",
              "104  896573f9bc2c6a695cb265df027c4f21214bc933  [KAFKA-14279]   \n",
              "105  2fad1652942226454a44038f2350642817f9f74b  [KAFKA-10199]   \n",
              "\n",
              "                                                   msg       data  lines  \\\n",
              "0                             KAFKA-1227 New producer!  28/1/2014  11720   \n",
              "1    KAFKA-1260 Integration Test for New Producer P...  27/2/2014    507   \n",
              "2    KAFKA-1328 New consumer APIs; reviewed by Jun ...  20/5/2014   1744   \n",
              "3                            KAFKA-1760: New consumer.  29/1/2015   5931   \n",
              "4            KAFKA-1915: Add checkstyle for java code.   3/2/2015   5440   \n",
              "..                                                 ...        ...    ...   \n",
              "101  KAFKA-10199: Cleanup TaskManager and Task inte...  21/7/2022    903   \n",
              "102  KAFKA-10199: Further refactor task lifecycle m...  27/7/2022   1065   \n",
              "103  KAFKA-10199: Handle task closure and recycling...  15/8/2022    667   \n",
              "104  KAFKA-14279: Add 3.3.x streams system tests (#...   9/1/2023   1365   \n",
              "105  KAFKA-10199: Add task updater metrics, part 1 ...  24/2/2023    509   \n",
              "\n",
              "     files                            critical_files  \\\n",
              "0      121                      [KafkaProducer.java]   \n",
              "1       14                      [KafkaProducer.java]   \n",
              "2       11  [KafkaConsumer.java, KafkaProducer.java]   \n",
              "3       89  [KafkaConsumer.java, KafkaProducer.java]   \n",
              "4       97  [KafkaConsumer.java, KafkaProducer.java]   \n",
              "..     ...                                       ...   \n",
              "101     16      [StreamTask.java, StreamThread.java]   \n",
              "102     18                         [StreamTask.java]   \n",
              "103     11                         [StreamTask.java]   \n",
              "104     14                      [StreamsConfig.java]   \n",
              "105      9                       [StreamThread.java]   \n",
              "\n",
              "                                       keywords  \\\n",
              "0                [TODO, TODO, TODO, TODO, TODO]   \n",
              "1    [TODO, TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "2          [TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "3                [TODO, TODO, TODO, TODO, TODO]   \n",
              "4          [TODO, TODO, TODO, TODO, TODO, TODO]   \n",
              "..                                          ...   \n",
              "101                          [TODO, TODO, TODO]   \n",
              "102                          [TODO, TODO, TODO]   \n",
              "103              [TODO, TODO, TODO, TODO, TODO]   \n",
              "104                                      [TODO]   \n",
              "105                          [TODO, TODO, TODO]   \n",
              "\n",
              "                                              comments  \\\n",
              "0    [        /* TODO: add docs */,                ...   \n",
              "1    [    // TODO: errorCode 4 for InvalidFetchSize...   \n",
              "2    [        /* TODO: add config docs */,         ...   \n",
              "3    [            // TODO: list offset call could b...   \n",
              "4    [        // TODO read uncompressed content siz...   \n",
              "..                                                 ...   \n",
              "101  [     * TODO: we should be able to not need th...   \n",
              "102  [     * TODO: we pass in the new input partiti...   \n",
              "103  [    // TODO: convert to StreamTask when we re...   \n",
              "104  [            // However, values are also use a...   \n",
              "105  [            // TODO (?) If we cannot fetch re...   \n",
              "\n",
              "                                           diff_commit  \\\n",
              "0    [{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...   \n",
              "1    [{'KafkaProducer.java': '@@ -33,6 +33,7 @@ imp...   \n",
              "2    [{'KafkaConsumer.java': '@@ -0,0 +1,575 @@\n",
              "+/*...   \n",
              "3    [{'KafkaConsumer.java': '@@ -9,380 +9,447 @@\n",
              " ...   \n",
              "4    [{'KafkaConsumer.java': '@@ -30,13 +30,13 @@ i...   \n",
              "..                                                 ...   \n",
              "101  [{'StreamTask.java': '@@ -127,7 +127,7 @@ publ...   \n",
              "102  [{'StreamTask.java': '@@ -83,7 +83,6 @@ public...   \n",
              "103  [{'StreamTask.java': '@@ -568,7 +568,7 @@ publ...   \n",
              "104  [{'StreamsConfig.java': '@@ -392,6 +392,12 @@ ...   \n",
              "105  [{'StreamThread.java': '@@ -404,7 +404,7 @@ pu...   \n",
              "\n",
              "                                        modified_files  \\\n",
              "0    [build.sbt, BufferExhaustedException.java, Cal...   \n",
              "1    [KafkaProducer.java, MockProducer.java, Record...   \n",
              "2    [Consumer.java, ConsumerConfig.java, ConsumerR...   \n",
              "3    [build.gradle, ClientRequest.java, ClusterConn...   \n",
              "4    [README.md, build.gradle, checkstyle.xml, impo...   \n",
              "..                                                 ...   \n",
              "101  [AbstractTask.java, ActiveTaskCreator.java, Pa...   \n",
              "102  [AbstractTask.java, ActiveTaskCreator.java, St...   \n",
              "103  [ActiveTaskCreator.java, StandbyTask.java, Sta...   \n",
              "104  [build.gradle, dependencies.gradle, settings.g...   \n",
              "105  [ChangelogReader.java, DefaultStateUpdater.jav...   \n",
              "\n",
              "                                   dict_modified_files  \n",
              "0    [@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...  \n",
              "1    [@@ -33,6 +33,7 @@ import org.apache.kafka.com...  \n",
              "2    [@@ -0,0 +1,125 @@\\n+/**\\n+ * Licensed to the ...  \n",
              "3    [@@ -369,6 +369,7 @@ project(':clients') {\\n  ...  \n",
              "4    [@@ -98,6 +98,9 @@ Please note for this to wor...  \n",
              "..                                                 ...  \n",
              "101  [@@ -20,6 +20,7 @@ import org.apache.kafka.com...  \n",
              "102  [@@ -43,10 +43,11 @@ public abstract class Abs...  \n",
              "103  [@@ -135,6 +135,7 @@ class ActiveTaskCreator {...  \n",
              "104  [@@ -2040,6 +2040,7 @@ project(':streams') {\\n...  \n",
              "105  [@@ -28,8 +28,10 @@ import java.util.Set;\\n pu...  \n",
              "\n",
              "[106 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ed920de2-40ef-440f-8d87-f4111ee2a071\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>commits</th>\n",
              "      <th>issues_ids</th>\n",
              "      <th>msg</th>\n",
              "      <th>data</th>\n",
              "      <th>lines</th>\n",
              "      <th>files</th>\n",
              "      <th>critical_files</th>\n",
              "      <th>keywords</th>\n",
              "      <th>comments</th>\n",
              "      <th>diff_commit</th>\n",
              "      <th>modified_files</th>\n",
              "      <th>dict_modified_files</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>269d16d3c915d09f650ae32aa81542bd8522ca68</td>\n",
              "      <td>[KAFKA-1227]</td>\n",
              "      <td>KAFKA-1227 New producer!</td>\n",
              "      <td>28/1/2014</td>\n",
              "      <td>11720</td>\n",
              "      <td>121</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        /* TODO: add docs */,                ...</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -0,0 +1,240 @@\n",
              "+pa...</td>\n",
              "      <td>[build.sbt, BufferExhaustedException.java, Cal...</td>\n",
              "      <td>[@@ -0,0 +1,11 @@\\n+import sbt._\\n+import Keys...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5e2a9a560d847bd0cf364d86bd6784f70d99c71a</td>\n",
              "      <td>[KAFKA-1260]</td>\n",
              "      <td>KAFKA-1260 Integration Test for New Producer P...</td>\n",
              "      <td>27/2/2014</td>\n",
              "      <td>507</td>\n",
              "      <td>14</td>\n",
              "      <td>[KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[    // TODO: errorCode 4 for InvalidFetchSize...</td>\n",
              "      <td>[{'KafkaProducer.java': '@@ -33,6 +33,7 @@ imp...</td>\n",
              "      <td>[KafkaProducer.java, MockProducer.java, Record...</td>\n",
              "      <td>[@@ -33,6 +33,7 @@ import org.apache.kafka.com...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>c24740c7b0f6a6e7c66659da786a346650b76766</td>\n",
              "      <td>[KAFKA-1328]</td>\n",
              "      <td>KAFKA-1328 New consumer APIs; reviewed by Jun ...</td>\n",
              "      <td>20/5/2014</td>\n",
              "      <td>1744</td>\n",
              "      <td>11</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        /* TODO: add config docs */,         ...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -0,0 +1,575 @@\n",
              "+/*...</td>\n",
              "      <td>[Consumer.java, ConsumerConfig.java, ConsumerR...</td>\n",
              "      <td>[@@ -0,0 +1,125 @@\\n+/**\\n+ * Licensed to the ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0699ff2ce60abb466cab5315977a224f1a70a4da</td>\n",
              "      <td>[KAFKA-1760]</td>\n",
              "      <td>KAFKA-1760: New consumer.</td>\n",
              "      <td>29/1/2015</td>\n",
              "      <td>5931</td>\n",
              "      <td>89</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[            // TODO: list offset call could b...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -9,380 +9,447 @@\n",
              " ...</td>\n",
              "      <td>[build.gradle, ClientRequest.java, ClusterConn...</td>\n",
              "      <td>[@@ -369,6 +369,7 @@ project(':clients') {\\n  ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1c6d5bbac672cbf49591aed0889510b10e3285fa</td>\n",
              "      <td>[KAFKA-1915]</td>\n",
              "      <td>KAFKA-1915: Add checkstyle for java code.</td>\n",
              "      <td>3/2/2015</td>\n",
              "      <td>5440</td>\n",
              "      <td>97</td>\n",
              "      <td>[KafkaConsumer.java, KafkaProducer.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[        // TODO read uncompressed content siz...</td>\n",
              "      <td>[{'KafkaConsumer.java': '@@ -30,13 +30,13 @@ i...</td>\n",
              "      <td>[README.md, build.gradle, checkstyle.xml, impo...</td>\n",
              "      <td>[@@ -98,6 +98,9 @@ Please note for this to wor...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>c9b6e19b3b37499de17da19e82b1b98e3b9f6b5c</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "      <td>KAFKA-10199: Cleanup TaskManager and Task inte...</td>\n",
              "      <td>21/7/2022</td>\n",
              "      <td>903</td>\n",
              "      <td>16</td>\n",
              "      <td>[StreamTask.java, StreamThread.java]</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[     * TODO: we should be able to not need th...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -127,7 +127,7 @@ publ...</td>\n",
              "      <td>[AbstractTask.java, ActiveTaskCreator.java, Pa...</td>\n",
              "      <td>[@@ -20,6 +20,7 @@ import org.apache.kafka.com...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>06f47c3b517f5c96c1bd981369843f3483947197</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "      <td>KAFKA-10199: Further refactor task lifecycle m...</td>\n",
              "      <td>27/7/2022</td>\n",
              "      <td>1065</td>\n",
              "      <td>18</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[     * TODO: we pass in the new input partiti...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -83,7 +83,6 @@ public...</td>\n",
              "      <td>[AbstractTask.java, ActiveTaskCreator.java, St...</td>\n",
              "      <td>[@@ -43,10 +43,11 @@ public abstract class Abs...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>dc72f6ec02c7a7fbda083cd8a5a9f0081c7e58fd</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "      <td>KAFKA-10199: Handle task closure and recycling...</td>\n",
              "      <td>15/8/2022</td>\n",
              "      <td>667</td>\n",
              "      <td>11</td>\n",
              "      <td>[StreamTask.java]</td>\n",
              "      <td>[TODO, TODO, TODO, TODO, TODO]</td>\n",
              "      <td>[    // TODO: convert to StreamTask when we re...</td>\n",
              "      <td>[{'StreamTask.java': '@@ -568,7 +568,7 @@ publ...</td>\n",
              "      <td>[ActiveTaskCreator.java, StandbyTask.java, Sta...</td>\n",
              "      <td>[@@ -135,6 +135,7 @@ class ActiveTaskCreator {...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>896573f9bc2c6a695cb265df027c4f21214bc933</td>\n",
              "      <td>[KAFKA-14279]</td>\n",
              "      <td>KAFKA-14279: Add 3.3.x streams system tests (#...</td>\n",
              "      <td>9/1/2023</td>\n",
              "      <td>1365</td>\n",
              "      <td>14</td>\n",
              "      <td>[StreamsConfig.java]</td>\n",
              "      <td>[TODO]</td>\n",
              "      <td>[            // However, values are also use a...</td>\n",
              "      <td>[{'StreamsConfig.java': '@@ -392,6 +392,12 @@ ...</td>\n",
              "      <td>[build.gradle, dependencies.gradle, settings.g...</td>\n",
              "      <td>[@@ -2040,6 +2040,7 @@ project(':streams') {\\n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>2fad1652942226454a44038f2350642817f9f74b</td>\n",
              "      <td>[KAFKA-10199]</td>\n",
              "      <td>KAFKA-10199: Add task updater metrics, part 1 ...</td>\n",
              "      <td>24/2/2023</td>\n",
              "      <td>509</td>\n",
              "      <td>9</td>\n",
              "      <td>[StreamThread.java]</td>\n",
              "      <td>[TODO, TODO, TODO]</td>\n",
              "      <td>[            // TODO (?) If we cannot fetch re...</td>\n",
              "      <td>[{'StreamThread.java': '@@ -404,7 +404,7 @@ pu...</td>\n",
              "      <td>[ChangelogReader.java, DefaultStateUpdater.jav...</td>\n",
              "      <td>[@@ -28,8 +28,10 @@ import java.util.Set;\\n pu...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>106 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed920de2-40ef-440f-8d87-f4111ee2a071')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ed920de2-40ef-440f-8d87-f4111ee2a071 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ed920de2-40ef-440f-8d87-f4111ee2a071');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-722416ed-16cd-4934-8d40-d28a8522b069\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-722416ed-16cd-4934-8d40-d28a8522b069')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-722416ed-16cd-4934-8d40-d28a8522b069 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(df_issues_in_commits_with_critical_classes_kl_satd.describe(), 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "_Gn41QJVzNe-",
        "outputId": "77795c9f-8c22-4cce-9f17-7416ca087603"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          lines   files\n",
              "count    106.00  106.00\n",
              "mean    1761.23   30.54\n",
              "std     2441.11   27.91\n",
              "min       10.00    2.00\n",
              "25%      474.75   11.00\n",
              "50%     1007.50   21.50\n",
              "75%     1992.75   39.00\n",
              "max    16103.00  143.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9bcc3b3b-c95c-4c2f-8d74-195483ab8285\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lines</th>\n",
              "      <th>files</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>106.00</td>\n",
              "      <td>106.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1761.23</td>\n",
              "      <td>30.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2441.11</td>\n",
              "      <td>27.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.00</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>474.75</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1007.50</td>\n",
              "      <td>21.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1992.75</td>\n",
              "      <td>39.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>16103.00</td>\n",
              "      <td>143.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9bcc3b3b-c95c-4c2f-8d74-195483ab8285')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9bcc3b3b-c95c-4c2f-8d74-195483ab8285 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9bcc3b3b-c95c-4c2f-8d74-195483ab8285');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8f9bad49-7347-4914-9ff5-5bec3fb46f8c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8f9bad49-7347-4914-9ff5-5bec3fb46f8c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8f9bad49-7347-4914-9ff5-5bec3fb46f8c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_columns = ['commits', 'issues_ids', 'msg', 'data', 'lines', 'files', 'critical_files', 'keywords', 'comments']\n",
        "df_issues_in_commits_with_critical_classes_kl_satd[my_columns].to_excel('issues_in_commits_with_critical_classes_kl_satd.xlsx', index=False)"
      ],
      "metadata": {
        "id": "Z6FM7JiHzgHy"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "list_commits_with_satd_keywords_with_critical_files_and_issues = df_issues_in_commits_with_critical_classes_kl_satd.issues_ids.to_list()"
      ],
      "metadata": {
        "id": "pLD-qvgObUF4"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.5 Planilha de issues que aparecem nos commits com arquivos criticos\n",
        "\n",
        "Cria uma planilha contendo todos os issues que aparecem em mensagens de commits que tenham arquivos críticos e também possuem keywords de SATD"
      ],
      "metadata": {
        "id": "1sAK4zyrmFSE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "l_issue_key_aux, l_issue_type_aux, l_issue_summary_aux, l_issue_description_aux, l_issue_status_aux, l_issue_priority_aux, l_issue_comments_aux = [], [], [], [], [], [], []\n",
        "\n",
        "for issue in my_jira_issues.get_issues():\n",
        "  for each in list_commits_with_satd_keywords_with_critical_files_and_issues:\n",
        "    if issue.key in each:\n",
        "      l_issue_key_aux.append(issue.key)\n",
        "      l_issue_type_aux.append(issue.issue_type)\n",
        "      l_issue_summary_aux.append(issue.summary)\n",
        "      l_issue_description_aux.append(issue.description)\n",
        "      l_issue_status_aux.append(issue.status)\n",
        "      l_issue_priority_aux.append(issue.priority)\n",
        "      texto_aux = \"\"\n",
        "      for item in issue.get_comments():\n",
        "        texto_aux = texto_aux + str(item) + \"\\n\"\n",
        "      l_issue_comments_aux.append(texto_aux)\n",
        "\n",
        "dict_issues_in_commits_detailed = {\n",
        " 'issue_key': l_issue_key_aux,\n",
        " 'issue_type':l_issue_type_aux,\n",
        " 'summary':l_issue_summary_aux,\n",
        " 'description':l_issue_description_aux,\n",
        " 'status':l_issue_status_aux,\n",
        " 'priority':l_issue_priority_aux,\n",
        " 'comments':l_issue_comments_aux\n",
        "}\n",
        "\n",
        "df_issues_in_commits_detailed = pd.DataFrame(dict_issues_in_commits_detailed)\n",
        "df_issues_in_commits_detailed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "lGXHtmpimHr4",
        "outputId": "eaae6183-bceb-4a17-a9a9-b1015a3c52ec"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       issue_key   issue_type  \\\n",
              "0     KAFKA-1260     Sub-task   \n",
              "1     KAFKA-1227  New Feature   \n",
              "2     KAFKA-1328     Sub-task   \n",
              "3     KAFKA-1760     Sub-task   \n",
              "4     KAFKA-1915  Improvement   \n",
              "..           ...          ...   \n",
              "105  KAFKA-13152  Improvement   \n",
              "106  KAFKA-13491     Sub-task   \n",
              "107  KAFKA-13785  Improvement   \n",
              "108  KAFKA-13654  Improvement   \n",
              "109  KAFKA-14279         Task   \n",
              "\n",
              "                                               summary  \\\n",
              "0    Integration Test for New Producer Part II: Bro...   \n",
              "1                            Code dump of new producer   \n",
              "2                                Add new consumer APIs   \n",
              "3                        Implement new consumer client   \n",
              "4                   Integrate checkstyle for java code   \n",
              "..                                                 ...   \n",
              "105  Replace \"buffered.records.per.partition\" & \"ca...   \n",
              "106                           Implement IQv2 Framework   \n",
              "107  Support emit final result for windowed aggrega...   \n",
              "108      Extend KStream process with new Processor API   \n",
              "109  Add 3.3.1 to broker/client and stream upgrade/...   \n",
              "\n",
              "                                           description    status priority  \\\n",
              "0                                                         Closed    Major   \n",
              "1    The plan is to take a dump of the producer cod...  Resolved    Major   \n",
              "2    New consumer API discussion is here - http://m...  Resolved    Major   \n",
              "3                         Implement a consumer client.  Resolved    Major   \n",
              "4    There are a lot of little style and layering p...  Resolved    Minor   \n",
              "..                                                 ...       ...      ...   \n",
              "105  The current config \"buffered.records.per.parti...  Reopened    Major   \n",
              "106   See https://cwiki.apache.org/confluence/x/85OqCw    Closed    Major   \n",
              "107  For KIP-825: https://cwiki.apache.org/confluen...    Closed    Major   \n",
              "108  Extending KStream#process to use latest Proces...  Resolved    Major   \n",
              "109  Per the penultimate bullet on the [release che...  Resolved  Blocker   \n",
              "\n",
              "                                              comments  \n",
              "0    Created reviewboard https://reviews.apache.org...  \n",
              "1    Created reviewboard https://reviews.apache.org...  \n",
              "2    Created reviewboard https://reviews.apache.org...  \n",
              "3    Created reviewboard https://reviews.apache.org...  \n",
              "4    Created reviewboard https://reviews.apache.org...  \n",
              "..                                                 ...  \n",
              "105  hey [~guozhang], i would like to take this up....  \n",
              "106                                                     \n",
              "107                                                     \n",
              "108                                                     \n",
              "109  hello,I'm new to kafka. Can I resolve this?\\nY...  \n",
              "\n",
              "[110 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0aea0343-ef4f-491e-a9ad-31fe08cfe8e9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>issue_key</th>\n",
              "      <th>issue_type</th>\n",
              "      <th>summary</th>\n",
              "      <th>description</th>\n",
              "      <th>status</th>\n",
              "      <th>priority</th>\n",
              "      <th>comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KAFKA-1260</td>\n",
              "      <td>Sub-task</td>\n",
              "      <td>Integration Test for New Producer Part II: Bro...</td>\n",
              "      <td></td>\n",
              "      <td>Closed</td>\n",
              "      <td>Major</td>\n",
              "      <td>Created reviewboard https://reviews.apache.org...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KAFKA-1227</td>\n",
              "      <td>New Feature</td>\n",
              "      <td>Code dump of new producer</td>\n",
              "      <td>The plan is to take a dump of the producer cod...</td>\n",
              "      <td>Resolved</td>\n",
              "      <td>Major</td>\n",
              "      <td>Created reviewboard https://reviews.apache.org...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>KAFKA-1328</td>\n",
              "      <td>Sub-task</td>\n",
              "      <td>Add new consumer APIs</td>\n",
              "      <td>New consumer API discussion is here - http://m...</td>\n",
              "      <td>Resolved</td>\n",
              "      <td>Major</td>\n",
              "      <td>Created reviewboard https://reviews.apache.org...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>KAFKA-1760</td>\n",
              "      <td>Sub-task</td>\n",
              "      <td>Implement new consumer client</td>\n",
              "      <td>Implement a consumer client.</td>\n",
              "      <td>Resolved</td>\n",
              "      <td>Major</td>\n",
              "      <td>Created reviewboard https://reviews.apache.org...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KAFKA-1915</td>\n",
              "      <td>Improvement</td>\n",
              "      <td>Integrate checkstyle for java code</td>\n",
              "      <td>There are a lot of little style and layering p...</td>\n",
              "      <td>Resolved</td>\n",
              "      <td>Minor</td>\n",
              "      <td>Created reviewboard https://reviews.apache.org...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>KAFKA-13152</td>\n",
              "      <td>Improvement</td>\n",
              "      <td>Replace \"buffered.records.per.partition\" &amp; \"ca...</td>\n",
              "      <td>The current config \"buffered.records.per.parti...</td>\n",
              "      <td>Reopened</td>\n",
              "      <td>Major</td>\n",
              "      <td>hey [~guozhang], i would like to take this up....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>KAFKA-13491</td>\n",
              "      <td>Sub-task</td>\n",
              "      <td>Implement IQv2 Framework</td>\n",
              "      <td>See https://cwiki.apache.org/confluence/x/85OqCw</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Major</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>KAFKA-13785</td>\n",
              "      <td>Improvement</td>\n",
              "      <td>Support emit final result for windowed aggrega...</td>\n",
              "      <td>For KIP-825: https://cwiki.apache.org/confluen...</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Major</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>KAFKA-13654</td>\n",
              "      <td>Improvement</td>\n",
              "      <td>Extend KStream process with new Processor API</td>\n",
              "      <td>Extending KStream#process to use latest Proces...</td>\n",
              "      <td>Resolved</td>\n",
              "      <td>Major</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>KAFKA-14279</td>\n",
              "      <td>Task</td>\n",
              "      <td>Add 3.3.1 to broker/client and stream upgrade/...</td>\n",
              "      <td>Per the penultimate bullet on the [release che...</td>\n",
              "      <td>Resolved</td>\n",
              "      <td>Blocker</td>\n",
              "      <td>hello,I'm new to kafka. Can I resolve this?\\nY...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0aea0343-ef4f-491e-a9ad-31fe08cfe8e9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0aea0343-ef4f-491e-a9ad-31fe08cfe8e9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0aea0343-ef4f-491e-a9ad-31fe08cfe8e9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a1f70fd9-f711-4ce2-91cb-f8b9559149ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a1f70fd9-f711-4ce2-91cb-f8b9559149ec')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a1f70fd9-f711-4ce2-91cb-f8b9559149ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Salva o issues_in_commits_with_critical_classes_satd_detailed em uma planilha excel')\n",
        "df_issues_in_commits_detailed.to_excel('kafka_issues_in_commits_with_critical_classes_satd_detailed.xlsx')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEIPsTX2mMsy",
        "outputId": "c22352ab-bd0f-4da2-99f0-647533e2453e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Salva o issues_in_commits_with_critical_classes_satd_detailed em uma planilha excel\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lista_issues_satd_in_commits_com_arquivos_criticos_temp = df_issues_in_commits_detailed.issue_key.to_list()\n",
        "\n",
        "ve_lista_com_issues_com_keywords_e_nos_commits_com_arquivos_criticos = []\n",
        "#5.4 Issues que aparecem nos commits com arquivos críticos\n",
        "for issue_key in lista_issues_satd_in_commits_com_arquivos_criticos_temp:\n",
        "  if issue_key not in ve_lista_com_issues_com_keywords_e_nos_commits_com_arquivos_criticos:\n",
        "    ve_lista_com_issues_com_keywords_e_nos_commits_com_arquivos_criticos.append(issue_key)\n",
        "\n",
        "ve_qtd_issues_com_keywords_e_nos_commits_com_arquivos_criticos = len(ve_lista_com_issues_com_keywords_e_nos_commits_com_arquivos_criticos)"
      ],
      "metadata": {
        "id": "11CvanSdgkGj"
      },
      "execution_count": 55,
      "outputs": []
    }
  ]
}