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Predicting Patch Correctness Based on the Similarity of Failing Test Cases

@article{tian2022predicting,
  title={Predicting Patch Correctness Based on the Similarity of Failing Test Cases},
  author={Tian, Haoye and Li, Yinghua and Pian, Weiguo and Kabore, Abdoul Kader and Liu, Kui and Habib, Andrew and Klein, Jacques and Bissyand{\'e}, Tegawend{\'e} F},
  journal={ACM Transactions on Software Engineering and Methodology},
  year={2022},
  publisher={ACM New York, NY},
  url = {https://doi.org/10.1145/3511096}, 
  doi = {10.1145/3511096}

Paper Link: https://dl.acm.org/doi/10.1145/3511096

BATS

BATS, an unsupervised learning based system to predict patch correctness by checking patch Behaviour Against failing Test Specification.

Ⅰ) Requirements

  • Data. Download the BATS_Dataset from data in Zenodo. Set up self.path_generated_patch in experiment/config.py with the path of the downloaded PatchCollectingV1_sliced.

  • Code2Vec representation model.

    1. Download the trained model and uncompress the file. wget https://s3.amazonaws.com/code2vec/model/java14m_model.tar.gz tar -xvzf java14m_model.tar.gz
    2. Update the variable MODEL_MODEL_LOAD_PATH in ./word2vector.py according to destination folder of trained model

  • BERT model.

    • BERT model client&server: 24-layer, 1024-hidden, 16-heads, 340M parameters. download it here.
    • Environment for BERT server (different from reproduction)
      • python 3.7
      • pip install tensorflow==1.14
      • pip install bert-serving-client==1.10.0
      • pip install bert-serving-server==1.10.0
      • pip install protobuf==3.20.1
      • Launch BERT server via bert-serving-start -model_dir "Path2BertModel"/wwm_cased_L-24_H-1024_A-16 -num_worker=2 -max_seq_len=360

Ⅱ) Reproduction

Follow the experiment/README.md to obtain the experimental results in the paper.

Ⅲ) Custom Prediction

To predict the correctness of your custom patches, you are welcome to use the prediction interface.

python main.py predict $cut-off $bug_id $path2patch

For instance:

python main.py predict 0.8 Chart_26 "path2dataset"/BATS_DataSet/PatchCollectingV1_sliced/PraPR/Correct/Chart/26/patch1