What Makes a Good Commit Message?
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Abstract (arXiv)
A key issue in collaborative software development is communication among developers. One modality of communication is a commit message, in which developers describe the changes they make in a repository. As such, commit messages serve as an "audit trail" by which developers can understand how the source code of a project has changed-and why. Hence, the quality of commit messages affects the effectiveness of communication among developers. Commit messages are often of poor quality as developers lack time and motivation to craft a good message. Several automatic approaches have been proposed to generate commit messages. However, these are based on uncurated datasets including considerable proportions of poorly phrased commit messages. In this multi-method study, we first define what constitutes a "good" commit message, and then establish what proportion of commit messages lack information using a sample of almost 1,600 messages from five highly active open source projects. We find that an average of circa 44% of messages could be improved, suggesting the use of uncurated datasets may be a major threat when commit message generators are trained with such data. We also observe that prior work has not considered semantics of commit messages, and there is surprisingly little guidance available for writing good commit messages. To that end, we develop a taxonomy based on recurring patterns in commit messages' expressions. Finally, we investigate whether "good" commit messages can be automatically identified; such automation could prompt developers to write better commit messages.
Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Feb 2022.
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Citing papers per year: 2023: 3; 2024: 4; 2025: 7; 2026: 5.
19 papers.
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- 2023From Commit Message Generation to History-Aware Commit Message Completioncited by 16
- 2024KADEL: Knowledge-Aware Denoising Learning for Commit Message Generationcited by 10
- 2025Towards an Understanding of Context Utilization in Code Intelligencecited by 10
- 2024CommitBench: A Benchmark for Commit Message Generationcited by 8
- 2025LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLMcited by 8
- 2023Can GPT-4 Replicate Empirical Software Engineering Research?cited by 7
- 2025Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metricscited by 4
- 2024COMET: Generating Commit Messages using Delta Graph Context Representationcited by 3
- 2023A Full-fledged Commit Message Quality Checker Based on Machine Learningcited by 2
- 2026Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requestscited by 2
- 2026Why Does CLAUDE.md Keep Growing? Catastrophic Remembering in Agentic Codingcited by 2
- 2025Mitigating Sensitive Information Leakage in LLMs4Code through Machine Unlearningcited by 1
7 more
- 2025CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detectioncited by 1
- 2026Pull Requests as a Training Signal for Repo-Level Code Editingcited by 1
- 2026Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMscited by 1
- 2024Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settingscited by 0
- 2025AI-Powered Commit Explorer (APCE)cited by 0
- 2025CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generationcited by 0
- 2026An Empirical Study of Downstream Adaptation for Agent Skillscited by 0
- 2026Why Does CLAUDE.md Keep Growing? Catastrophic Remembering in Agentic Coding11 Aug 2026
- 2026An Empirical Study of Downstream Adaptation for Agent Skills3 Jul 2026
- 2026Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMs22 Apr 2026
- 2026Pull Requests as a Training Signal for Repo-Level Code Editing7 Feb 2026
- 2026Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requests24 Jan 2026
- 2025LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLM1 Dec 2025
- 2025CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detection25 Nov 2025
- 2025CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation22 Sep 2025
- 2025Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics12 Aug 2025
- 2025AI-Powered Commit Explorer (APCE)21 Jul 2025
- 2025Towards an Understanding of Context Utilization in Code Intelligence11 Apr 2025
- 2025Mitigating Sensitive Information Leakage in LLMs4Code through Machine Unlearning9 Feb 2025
7 more
- 2024Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settings15 Oct 2024
- 2024CommitBench: A Benchmark for Commit Message Generation8 Mar 2024
- 2024COMET: Generating Commit Messages using Delta Graph Context Representation2 Feb 2024
- 2024KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation16 Jan 2024
- 2023Can GPT-4 Replicate Empirical Software Engineering Research?3 Oct 2023
- 2023A Full-fledged Commit Message Quality Checker Based on Machine Learning9 Sep 2023
- 2023From Commit Message Generation to History-Aware Commit Message Completion15 Aug 2023
- 2023From Commit Message Generation to History-Aware Commit Message Completioncited by 16
- 2024KADEL: Knowledge-Aware Denoising Learning for Commit Message Generationcited by 10
- 2025Towards an Understanding of Context Utilization in Code Intelligencecited by 10
- 2024CommitBench: A Benchmark for Commit Message Generationcited by 8
- 2023Can GPT-4 Replicate Empirical Software Engineering Research?cited by 7
- 2025Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metricscited by 4
- 2024COMET: Generating Commit Messages using Delta Graph Context Representationcited by 3
- 2023A Full-fledged Commit Message Quality Checker Based on Machine Learningcited by 2
- 2025CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detectioncited by 1
- 2026Pull Requests as a Training Signal for Repo-Level Code Editingcited by 1
- 2026Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMscited by 1
- 2024Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settingscited by 0
12 most cited of 13 All 13 (JSON)
- 2026Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMs22 Apr 2026
- 2026Pull Requests as a Training Signal for Repo-Level Code Editing7 Feb 2026
- 2025CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detection25 Nov 2025
- 2025Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics12 Aug 2025
- 2025AI-Powered Commit Explorer (APCE)21 Jul 2025
- 2025Towards an Understanding of Context Utilization in Code Intelligence11 Apr 2025
- 2024Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settings15 Oct 2024
- 2024CommitBench: A Benchmark for Commit Message Generation8 Mar 2024
- 2024COMET: Generating Commit Messages using Delta Graph Context Representation2 Feb 2024
- 2024KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation16 Jan 2024
- 2023Can GPT-4 Replicate Empirical Software Engineering Research?3 Oct 2023
- 2023A Full-fledged Commit Message Quality Checker Based on Machine Learning9 Sep 2023
12 newest of 13 All 13 (JSON)