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}
} 

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

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

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

similarity_calculation

patch similarity statistics and filetra for RQ1 and RQ2.

prediction

classifier of patch correctness for RQ3.