The Community for Technology Leaders
2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) (2017)
Urbana, IL, USA
Oct. 30, 2017 to Nov. 3, 2017
ISBN: 978-1-5386-3976-4
pp: 135-146
Siyuan Jiang , Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
Ameer Armaly , Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
Collin McMillan , Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
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.
INDEX TERMS
Algorithm design and analysis, Software, Natural languages, Software algorithms, Machine learning, Computer bugs, Prediction algorithms
CITATION

S. Jiang, A. Armaly and C. McMillan, "Automatically generating commit messages from diffs using neural machine translation," 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), Urbana, IL, USA, 2017, pp. 135-146.
doi:10.1109/ASE.2017.8115626
180 ms
(Ver 3.3 (11022016))