[ICML 2020] DrRepair: Learning to Repair Programs from Error Messages

Overview

DrRepair: Learning to Repair Programs from Error Messages

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)},  
}

Dependencies

  • 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
      

Data

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)
       ...

Train models

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

Evaluate models

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.

DeepFix

1. Start server

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.

2. Run model on DeepFix test

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.

SPoC

1. Start server

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.

2. Run model on SPoC test

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.

Acknowledgment

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.
Owner
Michihiro Yasunaga
PhD Student in Computer Science
Michihiro Yasunaga
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis

iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis Andreas Bl

CompVis Heidelberg 36 Dec 25, 2022
Implementation of Deep Deterministic Policy Gradiet Algorithm in Tensorflow

ddpg-aigym Deep Deterministic Policy Gradient Implementation of Deep Deterministic Policy Gradiet Algorithm (Lillicrap et al.arXiv:1509.02971.) in Ten

Steven Spielberg P 247 Dec 07, 2022
Learning the Beauty in Songs: Neural Singing Voice Beautifier; ACL 2022 (Main conference); Official code

Learning the Beauty in Songs: Neural Singing Voice Beautifier Jinglin Liu, Chengxi Li, Yi Ren, Zhiying Zhu, Zhou Zhao Zhejiang University ACL 2022 Mai

Jinglin Liu 257 Dec 30, 2022
The official repository for "Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning" paper.

Intermdiate layer matters - SSL The official repository for "Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning" paper. Downl

Aakash Kaku 35 Sep 19, 2022
Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning"

CAPGNN Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning" Paper URL: https://ar

1 Mar 12, 2022
GitHub repository for the ICLR Computational Geometry & Topology Challenge 2021

ICLR Computational Geometry & Topology Challenge 2022 Welcome to the ICLR 2022 Computational Geometry & Topology challenge 2022 --- by the ICLR 2022 W

42 Dec 13, 2022
PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML)

pytorch-maml This is a PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML): https://arxiv

Kate Rakelly 516 Jan 05, 2023
tensorrt int8 量化yolov5 4.0 onnx模型

onnx模型转换为 int8 tensorrt引擎

123 Dec 28, 2022
This is the implementation of the paper "Self-supervised Outdoor Scene Relighting"

Self-supervised Outdoor Scene Relighting This is the implementation of the paper "Self-supervised Outdoor Scene Relighting". The model is implemented

Ye Yu 24 Dec 17, 2022
Building blocks for uncertainty-aware cycle consistency presented at NeurIPS'21.

UncertaintyAwareCycleConsistency This repository provides the building blocks and the API for the work presented in the NeurIPS'21 paper Robustness vi

EML Tübingen 19 Dec 12, 2022
ByteTrack(Multi-Object Tracking by Associating Every Detection Box)のPythonでのONNX推論サンプル

ByteTrack-ONNX-Sample ByteTrack(Multi-Object Tracking by Associating Every Detection Box)のPythonでのONNX推論サンプルです。 ONNXに変換したモデルも同梱しています。 変換自体を試したい方はByteT

KazuhitoTakahashi 16 Oct 26, 2022
Pytorch Lightning code guideline for conferences

Deep learning project seed Use this seed to start new deep learning / ML projects. Built in setup.py Built in requirements Examples with MNIST Badges

Pytorch Lightning 1k Jan 02, 2023
A Simple Key-Value Data-store written in Python

mercury-db This is a File Based Key-Value Datastore that supports basic CRUD (Create, Read, Update, Delete) operations developed using Python. The dat

Vaidhyanathan S M 1 Jan 09, 2022
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting This is the origin Pytorch implementation of Informer in the followin

Haoyi 3.1k Dec 29, 2022
Contains a bunch of different python programm tasks

py_tasks Contains a bunch of different python programm tasks Armstrong.py - calculate Armsrong numbers in range from 0 to n with / without cache and c

Dmitry Chmerenko 1 Dec 17, 2021
A curated list of awesome deep long-tailed learning resources.

A curated list of awesome deep long-tailed learning resources.

vanint 210 Dec 25, 2022
SeqTR: A Simple yet Universal Network for Visual Grounding

SeqTR This is the official implementation of SeqTR: A Simple yet Universal Network for Visual Grounding, which simplifies and unifies the modelling fo

seanZhuh 76 Dec 24, 2022
GMFlow: Learning Optical Flow via Global Matching

GMFlow GMFlow: Learning Optical Flow via Global Matching Authors: Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Dacheng Tao We streamline the

Haofei Xu 298 Jan 04, 2023
The offcial repository for 'CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos', SIGIR2022

CharacterBERT-DR The offcial repository for CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos, Sh

ielab 11 Nov 15, 2022
Robust Partial Matching for Person Search in the Wild

APNet for Person Search Introduction This is the code of Robust Partial Matching for Person Search in the Wild accepted in CVPR2020. The Align-to-Part

Yingji Zhong 36 Dec 18, 2022