Neural Program Repair with Execution-based Backpropagation (ICSE 2022)
@inproceedings{Ye2021RewardRepair,
title = {Neural Program Repair with Execution-based Backpropagation},
year = {2022},
author = {He Ye and Matias Martinez and Martin Monperrus},
url = {http://arxiv.org/pdf/2105.04123},
doi = {10.1145/3510003.3510222},
booktitle = {Proceedings of the International Conference on Software Engineering},
}
We share our trained model in Zenodo:
RewardRepair trained model: https://doi.org/10.5281/zenodo.5997686
├── data: csv data used for training
│
├── model: the trained model of RewardRepair
│
├── train.py: script to train RewardRepair
│
├── test.py: script to test RewardRepair
│
├── result: raw experiment results
│
├── ComparisonData.csv: the file to compare with the state-of-the-art
- JDK 1.8
- Pytorch==1.7.1
- transformers>=4.10.0
- OS: Linux and Mac
- Add submodule Bears and copy the folder myscript to Bears
git submodule add https://github.com/bears-bugs/bears-benchmark.git
cp -rf myscripts ./bears-benchmark/
pip install transformers
pip install sentencepiece
Get the CoCoNut java dataset from here: https://github.com/lin-tan/CoCoNut-Artifact/releases/tag/training_data_1.0.0 use the script to extract data: extract_CoCoNut_data.py
Data availables here:
https://zenodo.org/record/7009192#.YwPS4-xBxbg
Get Megadiff dataset from here https://github.com/monperrus/megadiff use the script to extract data: extract_Megadiff_data.py
Get CodRep dataset from here https://github.com/KTH/codrep-2019
To run our script
python3 train.py
To evaluate our model on other bugs (make sure the script takes the correct test file patch)
python3 test.py
| Benchmarks | Top30 | Top100 | Top200 |
|---|---|---|---|
| QuixBugs | 44.1% | 35.7% | 31.5% |
| Defects4J | 45.7% | 38.0% | 33.6% |
Note: Above are the number reported in the paper. The actual compilable rate should be higher. Because we found many uncompilable cases due to space in operators > =, < =, = =, ! =. This could be easily avoided by added tokens in training or post processing in test.py.