SequenceR is a seq2seq model designed to predict source code change on line level.
If you use SequenceR for academic purposes, please cite the following publication:
@article{chen2018sequencer,
title={SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair},
author={Chen, Zimin and Kommrusch, Steve and Tufano, Michele and Pouchet, Louis-No{\"e}l and Poshyvanyk, Denys and Monperrus, Martin},
journal={arXiv preprint arXiv:1901.01808},
year={2018}
}
Simply run the following two commands:
docker build --tag=sequencer .
docker run -it sequencerAnd now all dependecies are installed (including defects4j).
First run src/setup_env.sh to setup enviroment and clone/compile project. Please view src/setup_env.sh for more details.
Then run src/end-to-end.sh with the following parameters:
./end-to-end.sh --buggy_file=[abs path] --buggy_line=[int] --beam_size=[int] --output=[abs path]- --buggy_file: Absolute path to buggy file
- --buggy_line: Line number indicating where the bug is, or just want it get changed.
- --beam_size: Beam size for prediction
- --output: Output directory to store the generated patches
In results/Defects4J_patches you can find all patches that are found by SequencerR. Patches that are stored in *_compiled are patches that compiled. Patches that are stored in *_passed are patches that compiled and passed the test suite. Patches that are stored in *_correct are patches that compiled, passed the test suite and are equivalent to the human patch.
To rerun our experiment of SequenceR over Defects4J. Run src/Defects4J_Experiment/Defects4J_experiment.sh, make sure you have defects4j installed.
Defects4J_oneLiner_metadata.csv contains metadata for all Defects4J bugs that we consider. src/Defects4J_Experiment/validatePatch.py contains the precedure for running Defects4J test, we have time limit on compile time (60s) and test running time (300s).