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C# Data Extraction for "Learning to Represent Edits"
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DumpCommitData
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README.md

Learning to Represent Edits

This repo contains scripts to extract the Github code edits datasets used in "Learning to Represent Edits" by Yin et al., 2018.

Usage

First, create a conda environment that includes all required libraries.

conda env create -f environment.yml

source activate github_edits  # activate the environment

You also need to install dotnet core 2.1.

Run the script run.sh in the repo's root folder

./run.sh

This script will (1) crawl the Github to clone repos listed in sampled_repos.txt, (2) extract commits using DumpCommitData/extract_commits.py; (3) filter the extracted commits and perform cannonicalization, and extract the Abstract Syntax Tree of the previous and updated code in a commit (e.g., renaming locally defined variables)

The final output file DumpCommitData/github_commits.dataset.jsonl is a jsonl file, with each line consisting of a json-serialized entry. The format is:

Field Description
Id Id of the entry, format is `{ProjectName}
PrevCodeChunk Untokenized previous code (i.e., code before editing)
UpdatedCodeChunk Untokenized updated code (i.e., code after editing)
PrevCodeChunkTokens Tokenized previous code
UpdatedCodeChunkTokens Tokenized updated code
PrevCodeAST Json-serialized Abstract Syntax Tree of the previous code
UpdatedCodeAST Json-serialized Abstract Syntax Tree of the updated code
PrecedingContext Tokenized 3 lines of code before the edit
SucceedingContext Tokenized 3 lines of code after the edit

Citing

If you use this extractor in an academic work, please consider citing


@article{yin2018learning,
   author = {{Yin}, P. and {Neubig}, G. and {Allamanis}, M. and {Brockschmidt}, M. and {Gaunt}, A.~L.},
   title = "{Learning to Represent Edits}",
   journal = {ArXiv e-prints},
   archivePrefix = "arXiv",
   eprint = {1810.13337},
   year = 2018,
   month = oct,
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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