Towards Automatic Generation of Short Summaries of Commits
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Abstract (arXiv)
Committing to a version control system means submitting a software change to the system. Each commit can have a message to describe the submission. Several approaches have been proposed to automatically generate the content of such messages. However, the quality of the automatically generated messages falls far short of what humans write. In studying the differences between auto-generated and human-written messages, we found that 82% of the human-written messages have only one sentence, while the automatically generated messages often have multiple lines. Furthermore, we found that the commit messages often begin with a verb followed by an direct object. This finding inspired us to use a "verb+object" format in this paper to generate short commit summaries. We split the approach into two parts: verb generation and object generation. As our first try, we trained a classifier to classify a diff to a verb. We are seeking feedback from the community before we continue to work on generating direct objects for the commits.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Mar 2017.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 1; 2024: 1; 2025: 1.
8 papers.
- 2021A Survey on Machine Learning Techniques for Source Code Analysiscited by 24
- 2021On the Evaluation of Commit Message Generation Models: An Experimental Studycited by 20
- 2021CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Modelcited by 16
- 2023From Commit Message Generation to History-Aware Commit Message Completioncited by 16
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 15
- 2022AutoTSG: Learning and Synthesis for Incident Troubleshootingcited by 12
- 2024CommitBench: A Benchmark for Commit Message Generationcited by 8
- 2021A Sketch-Based Neural Model for Generating Commit Messages from Diffscited by 0
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence23 Nov 2025
- 2024CommitBench: A Benchmark for Commit Message Generation8 Mar 2024
- 2023From Commit Message Generation to History-Aware Commit Message Completion15 Aug 2023
- 2022AutoTSG: Learning and Synthesis for Incident Troubleshooting26 May 2022
- 2021A Survey on Machine Learning Techniques for Source Code Analysis18 Oct 2021
- 2021On the Evaluation of Commit Message Generation Models: An Experimental Study12 Jul 2021
- 2021CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model29 May 2021
- 2021A Sketch-Based Neural Model for Generating Commit Messages from Diffs8 Apr 2021