Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/126366
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Type: Conference paper
Title: Automatic generation of pull request descriptions
Author: Liu, Z.
Xia, X.
Treude, C.
Lo, D.
Li, S.
Citation: Proceedings: 2019 34th IEEE/ACM International Conference on Automated Software Engineering, 2019, pp.176-188
Publisher: IEEE
Publisher Place: Los Alamitos, CA
Issue Date: 2019
ISBN: 9781728125084
ISSN: 1938-4300
2643-1572
Conference Name: Automated Software Engineering Conference (ASE) (11 Nov 2019 - 15 Nov 2019 : San Diego, CA)
Statement of
Responsibility: 
Zhongxin Liu, Xin Xia, Christoph Treude, David Lo, Shanping Li
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.
Rights: © 2019 IEEE
DOI: 10.1109/ASE.2019.00026
Grant ID: http://purl.org/au-research/grants/arc/DE180100153
Published version: http://dx.doi.org/10.1109/ase.2019.00026
Appears in Collections:Aurora harvest 8
Computer Science publications

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