Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2019

Abstract

Enabled by the pull-based development model, developers can easily contribute to a project through pull requests (PRs). When creating a PR, developers can add a free-form description to describe what changes are made in this PR and/or why. Such a description is helpful for reviewers and other developers to gain a quick understanding of the PR without touching the details and may reduce the possibility of the PR being ignored or rejected. However, developers sometimes neglect to write descriptions for PRs. For example, in our collected dataset with over 333K PRs, more than 34% of the PR descriptions are empty. To alleviate this problem, we propose an approach to automatically generate PR descriptions based on the commit messages and the added source code comments in the PRs. We regard this problem as a text summarization problem and solve it using a novel sequence-to-sequence model. To cope with out-of-vocabulary words in software artifacts and bridge the gap between the training loss function of the sequence-to-sequence model and the evaluation metric ROUGE, which has been shown to correspond to human evaluation, we integrate the pointer generator and directly optimize for ROUGE using reinforcement learning and a special loss function. We build a dataset with over 41K PRs and evaluate our approach on this dataset through ROUGE and a human evaluation. Our evaluation results show that our approach outperforms two baselines by significant margins.

Keywords

Document Generation, Pull Request, Sequence to Sequence Learning

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ASE '19: Proceedings of the 34th ACM/IEEE International Conference on Automated Software Engineering, San Diego, November 11-15

First Page

176

Last Page

188

ISBN

9781728125084

Identifier

10.1109/ASE.2019.00026

Publisher

ACM

City or Country

New York

Embargo Period

8-2-2023

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ASE.2019.00026

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