2017/08/30 by Siyuan Jiang, Jiang, Siyuan, Ameer Armaly +3 · 2 voices · 14 citations
Computer Science · #Advanced Software Engineering Methodologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Software Engineering (cs.SE) #Software Engineering Research #Software Reliability and Analysis Research #cs.CL #cs.SE
paper · pdf · doi:10.48550/arxiv.1708.09492
Preprint version. Accepted in ASE 2017, the 32nd IEEE/ACM International Conference on Automated Software Engineering
arxiv created 2017/08/30 · openalex publication_date 2017/08/30 · arxiv published 2017/08/30 · arxiv updated 2017/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.