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PatchNet: Hierarchical Deep Learning-Based Stable Patch Identification for the Linux Kernel [pdf]

Contact

Questions and discussion are welcome: [email protected]

Implementation Environment

Please install the neccessary libraries before running our tool:

  • python==3.6.9
  • torch==1.2.0
  • tqdm==4.46.1
  • nltk==3.4.5
  • numpy==1.16.5
  • scikit-learn==0.22.1

Data & Pretrained models:

Please following the link below to download the data and pretrained models of our paper.

After downloading, simply copy the data and model folders to PatchNet folder.

Hyperparameters:

We have a number of different parameters

  • --embedding_dim: Dimension of embedding vectors.
  • --filter_sizes: Sizes of filters used by the convolutional neural network.
  • --num_filters: Number of filters.
  • --hidden_layers: Number of hidden layers.
  • --dropout_keep_prob: Dropout for training.
  • --l2_reg_lambda: Regularization rate.
  • --learning_rate: Learning rate.
  • --batch_size: Batch size.
  • --num_epochs: Number of epochs.

Running and evalutation

  • To train the model for bug fixing patch classification, please follow this command:

    $ python main.py -train -train_data [path of our data] -dictionary_data [path of our dictionary data]
    
  • To evaluate the model for bug fixing patch classification, please follow this command:

     $ python main.py -predict -pred_data [path of our data] -dictionary_data [path of our dictionary data] -load_model [path of our model]
    

Contact

Questions and discussion are welcome: [email protected]

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