This repo provides the source code & data of our paper: Graph-based, Self-Supervised Program Repair from Diagnostic Feedback (ICML 2020).
@InProceedings{Yasunaga20DrRepair,
author = {Michihiro Yasunaga and Percy Liang},
title = {Graph-based, Self-Supervised Program Repair from Diagnostic Feedback},
year = {2020},
booktitle = {International Conference on Machine Learning (ICML)},
}
- GCC: Follow the SPoC requirement (https://github.com/Sumith1896/spoc)
- Python 3.6.8 (e.g.
conda create -n DrRepair python=3.6.8) - Python libraries
- torch==1.0.1, numpy, tqdm, regex, joblib, pyyaml, bottle, cheroot, tensorboardX
- clang==8.0.1 (do the following)
conda config --add channels conda-forge conda install python-clang==8.0.1
Download all the raw data -- DeepFix, SPoC, codeforce (for pretraining) -- by
./download_raw_data.sh
You can preprocess the raw data to get the program repair data by running the commands in
data/1.run-gen-err-dataset--orig-spoc.sh
data/2.run-gen-err-dataset--auto-corrupt--spoc.sh
data/3.run-gen-err-dataset--auto-corrupt--deepfix.sh
However, this takes a significant time, so for your convenience, you can download all the preprocessed data by
./download_preprocessed_data.sh
The repo structure looks like the following:
.
└─ raw_data/
├── codeforce_data/ (raw programs from codeforce)
├── deepfix_data/ (raw programs from deepfix)
└── spoc_data/
├── spoc (SPoC data release)
└── translation_preds (line-level code predictions from Kulal+19)
└─ data/
├── *.sh, *.py (preprocessing scripts)
├── err-data-compiler--orig-spoc/ (preprocessed, program repair data for spoc)
├── err-dev-compiler--for-SPoC/ (└─ dev data for spoc)
├── err-vocab-compiler--for-SPoC/ (└─ vocab for spoc)
...
... [similarly for deepfix and pre-training]
└─ utils/ (utilities for code processing)
└─ model/ (DrRepair model)
└─ evaluation/ (to evaluate Repair model on deepfix/spoc test)
├── deepfix
└── spoc
├── translation_preds_test/ (line-level code predictions from Kulal+19 for TestP/TestW)
...
Let's train program repair models.
First, go to model directory.
Then, run commands listed in run_deepfix.sh or run_spoc.sh.
For example, if we train DrRepair ("base + graph" in the paper) on the DeepFix data, run:
name="code-compiler--2l-graph"
mkdir -p out_deepfix/${name}
python3 -u main_deepfix.py -o ${name} train \
configs/base.yml configs/data-deepfix/err-data-orig.yml \
configs/model-code-compiler/2l-graph--dec-attn-all.yml
We run the trained program repair model as a server. We then call this model on application tasks (DeepFix and SPoC) to evaluate the usefulness of the model.
First, go to model directory.
We run a trained model (e.g. code-compiler--2l-graph) as a server by
name="SERVER--code-compiler--2l-graph"
mkdir out_deepfix/${name}
python3 -u main_deepfix.py -o ${name} server -p <port> \
-l out_deepfix/code-compiler--2l-graph/<checkpoint> \
configs/base.yml configs/data-deepfix/err-data-orig.yml \
configs/model-code-compiler/2l-graph--dec-attn-all.yml
For <port>, pick a port number (e.g. 8080) for the server.
For <checkpoint>, pick a checkpoint (e.g. 150000) of the trained model.
Then run ifconfig to get the IP address (e.g. 172.24.67.161) of the machine hosting this model.
Concrete examples are provided in the second half of model/run_deepfix.sh.
Go to evaluation/deepfix directory. First prepare:
repo_root="../../../.."
program_data_root=${repo_root}"/raw_data/deepfix_data"
test_split_root=${repo_root}"/data/err-data-compiler--auto-corrupt--orig-deepfix/bin4"
To run the trained model on the DeepFix test examples, do
name="code-compiler--2l-graph"
mkdir -p out/${name}/log
cd out/${name}
for entry in ${test_split_root}/*
do
probid=`basename $entry`
python3 -u ../../test_deepfix.py \
--input-code-dir ${program_data_root}/${probid}/erroneous \
--repairer-server http://<IP>:<port>/pred
done
where you plug the IP address and port number into <IP> and <port>.
After this completes, you can get the test accuracy by
python3 -u ../../collate_deepfix.py
Concrete examples are provided in evaluation/run_test_deepfix.sh.
First, go to model directory.
We run a trained model (e.g. code-compiler--2l-graph--finetune) as a server by
name="SERVER--code-compiler--2l-graph--finetune"
mkdir out_spoc/${name}
python3 -u main_spoc.py -o ${name} server -p <port> \
-l out_spoc/code-compiler--2l-graph--finetune/<checkpoint> \
configs/base.yml configs/data-spoc/err-data-orig.yml \
configs/model-code-compiler/2l-graph--dec-attn-all.yml
Similar to DeepFix, pick a port number and a checkpoint, and get the IP address.
Concrete examples are provided in the second half of model/run_spoc.sh.
Go to evaluation/spoc directory. First prepare:
repo_root="../../../.."
To run the trained model on all the programs in SPoC TestW, do
name="code-compiler--2l-graph--finetune"
INPUT=translation_preds_test/testw #change to testp if you want to evaluate on testp
N=$(tail -n+2 ${INPUT}.tsv | cut -f 3-6 | uniq | wc -l) # Count the number of programs
interval=10
mkdir -p out_testw/${name}/log #change to testp if you want to evaluate on testp
cd out_testw/${name} #change to testp if you want to evaluate on testp
i=1
while [[ $i -le $N ]]; do
python -u ../../test_spoc.py -p 100 \
--compile-budget 100 --n-parallel ${interval} \
--repairer-server http://<IP>:<port>/pred \
../../${INPUT} $i
i=$(($i + ${interval}))
done
where you plug the IP address and port number into <IP> and <port>.
After this completes, you can get the test accuracy by
python3 -u ../../collate_spoc.py
Concrete examples are provided in evaluation/run_test_spoc.sh.
The original DeepFix and SPoC data used in this work come from the following papers:
DeepFix: Fixing common C language errors by deep learning. Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade. AAAI 2017.
SPoC: Search-based Pseudocode to Code. Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken and Percy Liang. NeurIPS 2019.
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