Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair

@inproceedings{tian2020evaluating, 
  title={Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair}, 
  author={Tian, Haoye and Liu, Kui and Kabor{\'e}, Abdoul Kader and Koyuncu, Anil and Li, Li and Klein, Jacques and Bissyand{\'e}, Tegawend{\'e} F.},
  booktitle={Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering}, 
  year={2020}, 
  publisher={ACM},
  url = {https://doi.org/10.1145/3324884.3416532}, 
  doi = {10.1145/3324884.3416532}
} 

Paper Link: https://ieeexplore.ieee.org/abstract/document/9286101

Ⅰ) Catalogue of Repository

• data

  the dataset and results of each experiment.

  • experiemnt1
    • Patches_train.zip: the developer patches as committed in five open source project repositories.
    • APR-Efficiency-PFL: the patches under the folders affixed with '_C'.
  • experiment2
    • The patches to be evaluated from RepairThemAll.
  • experiment3
    • APR-Efficiency-NFL: the patches labeled with affix '_P' and '_C', means 'palusible' and 'correct'.
    • DefectRepairing: the patches labeled with json file.
    • defects4j-developer: the correct patches.

• preprocess

  the preprocess of code file and data generation for RQ1 and RQ2.

• similarity_calculation

  the patch similarity statistics and filetra for RQ1 and RQ2.

• prediction

  the classifier of patch correctness for RQ3.

Ⅱ) Custom Prediction

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

A) Requirements for BERT

• 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
• Patch snippet text: your patch snippet.

B) Predict

Let's give it a try!

python API.py predict $patch_text