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Cited by 255

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Automatically generating commit messages from diffs using neural machine translation

Open access
Jiang, Siyuan, Armaly, Ameer, McMillan, Collin·2017·2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)
DOI 10.1109/ase.2017.8115626
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Abstract

Commit messages are a valuable resource in comprehension of software evolution, since they provide a record of changes such as feature additions and bug repairs. Unfortunately, programmers often neglect to write good commit messages. Different techniques have been proposed to help programmers by automatically writing these messages. These techniques are effective at describing what changed, but are often verbose and lack context for understanding the rationale behind a change. In contrast, humans write messages that are short and summarize the high level rationale. In this paper, we adapt Neural Machine Translation (NMT) to automatically "translate" diffs into commit messages. We trained an NMT algorithm using a corpus of diffs and human-written commit messages from the top 1k Github projects. We designed a filter to help ensure that we only trained the algorithm on higher-quality commit messages. Our evaluation uncovered a pattern in which the messages we generate tend to be either very high or very low quality. Therefore, we created a quality-assurance filter to detect cases in which we are unable to produce good messages, and return a warning instead.

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Metadata

Authors
Jiang, S., Armaly, A., & McMillan, C.
Venue
2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)
Publisher
IEEE
Published
2017-10
Pages
135-146
DOI
10.1109/ase.2017.8115626
OpenAlex
W2964322208
Cited by
255
References
10
FWCI
14.82
Type
conference paper
Language
en
Open access
green

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