CodeBLEU

Unofficial CodeBLEU implementation with Linux and MacOS supports available with PyPI and HF HUB.

Based on original CodeXGLUE/CodeBLEU code -- refactored, build for macos, tested and fixed multiple crutches to make it more usable.

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Metric Description

An ideal evaluation metric should consider the grammatical correctness and the logic correctness. We propose weighted n-gram match and syntactic AST match to measure grammatical correctness, and introduce semantic data-flow match to calculate logic correctness.
(from CodeXGLUE repo)

In a nutshell, CodeBLEU is a weighted combination of n-gram match (BLEU), weighted n-gram match (BLEU-weighted), AST match and data-flow match scores.

The metric has shown higher correlation with human evaluation than BLEU and accuracy metrics.

Usage

from codebleu import calc_codebleu

prediction = "def add ( a , b ) :\n return a + b"
reference = "def sum ( first , second ) :\n return second + first"

result = calc_codebleu([reference], [prediction], lang="python", weights=(0.25, 0.25, 0.25, 0.25), tokenizer=None)
print(result)
# {
#   'codebleu': 0.5537, 
#   'ngram_match_score': 0.1041, 
#   'weighted_ngram_match_score': 0.1109, 
#   'syntax_match_score': 1.0, 
#   'dataflow_match_score': 1.0
# }

where calc_codebleu takes the following arguments:

• refarences (list[str] or list[list[str]]): reference code
• predictions (list[str]) predicted code
• lang (str): code language, see codebleu.AVAILABLE_LANGS for available languages (python, c_sharp, java at the moment)
• weights (tuple[float,float,float,float]): weights of the ngram_match, weighted_ngram_match, syntax_match, and dataflow_match respectively, defaults to (0.25, 0.25, 0.25, 0.25)
• tokenizer (callable): to split code string to tokens, defaults to s.split()

and outputs the dict[str, float] with following fields:

• codebleu: the final CodeBLEU score
• ngram_match_score: ngram_match score (BLEU)
• weighted_ngram_match_score: weighted_ngram_match score (BLEU-weighted)
• syntax_match_score: syntax_match score (AST match)
• dataflow_match_score: dataflow_match score

Alternatively, you can use k4black/codebleu from HuggingFace Spaces:

import evaluate
metric = evaluate.load("dvitel/codebleu")

result = metric.compute([reference], [prediction], lang="python", weights=(0.25, 0.25, 0.25, 0.25))

Feel free to check the HF Space with online example: k4black/codebleu

Installation

Requires Python 3.8+

The metrics can be installed with pip and used as indicated above:

pip install codebleu

alternatively the metric is available as k4black/codebleu in evaluate (lib installation required):

import evaluate
metric = evaluate.load("dvitel/codebleu")

Citation

Official CodeBLEU paper can be cited as follows:

@misc{ren2020codebleu,
      title={CodeBLEU: a Method for Automatic Evaluation of Code Synthesis}, 
      author={Shuo Ren and Daya Guo and Shuai Lu and Long Zhou and Shujie Liu and Duyu Tang and Neel Sundaresan and Ming Zhou and Ambrosio Blanco and Shuai Ma},
      year={2020},
      eprint={2009.10297},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}