Commit Message Matters: Investigating Impact and Evolution of Commit Message Quality
Jiawei Li, Iftekhar Ahmed
Abstract
Commit messages play an important role in communication among developers. To measure the quality of commit messages, researchers have defined what semantically constitutes a Good commit message: it should have both the summary of the code change (What) and the motivation/reason behind it (Why). The presence of the issue report/pull request links referenced in a commit message has been treated as a way of providing Why information. In this study, we found several quality issues that could hamper the links' ability to provide Why information. Based on this observation, we developed a machine learning classifier for automatically identifying whether a commit message has What and Why information by considering both the commit messages and the link contents. This classifier outperforms state-of-the-art machine learning classifiers by 12 percentage points improvement in the F1 score. With the improved classifier, we conducted a mixed method empirical analysis and found that: (1) Commit message quality has an impact on software defect proneness, and (2) the overall quality of the commit messages decreases over time, while developers believe they are writing better commit messages. All the research artifacts (i.e., tools, scripts, and data) of this study are available on the accompanying website [2].
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext d8bf9cff-b08c-4b38-b3e9-b949e264de95Cited by top-tier papers7
- Do CONTRIBUTING Files Provide Information about OSS Newcomers' Onboarding Barriers?Felipe Fronchetti, David C. Shepherd, Igor Wiese, Christoph Treude et al.FSE 2023 · 23 citations
- From Commit Message Generation to History-Aware Commit Message CompletionAleksandra Eliseeva, Yaroslav Sokolov, Egor Bogomolov, Yaroslav Golubev et al.ASE 2023 · 22 citations
- An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far are We?Hyunjae Suh, Mahan Tafreshipour, Jiawei Li, Adithya Bhattiprolu et al.ICSE 2025 · 2 citations
- An Empirical Study on Commit Message Generation Using LLMs via In-Context LearningYifan Wu, Yunpeng Wang, Ying Li, Wei Tao et al.ICSE 2025 · 1 citation
- Inside Out: Uncovering How Comment Internalization Steers LLMs for Better or WorseAaron Imani, Mohammad Moshirpour, Iftekhar AhmedICSE 2026
Builds on4
- What Makes a Good Commit Message?Yingchen Tian, Yuxia Zhang, Klaas-Jan Stol, Lin Jiang et al.ICSE 2022 · 90 citations
- Evaluating SZZ Implementations Through a Developer-informed OracleGiovanni Rosa, Luca Pascarella, Simone Scalabrino, Rosalia Tufano et al.ICSE 2021 · 42 citations
- On the relationship between design discussions and design quality: a case study of Apache projectsUmme Ayda Mannan, Iftekhar Ahmed, Carlos Jensen, Anita SarmaFSE 2020 · 15 citations
- Why Security Defects Go Unnoticed during Code Reviews? A Case-Control Study of the Chromium OS ProjectRajshakhar Paul, Asif Kamal Turzo, Amiangshu BosuICSE 2021 · 2 citations
Related papers
- An Empirical Study on Learning-based Techniques for Explicit and Implicit Commit Messages GenerationZhiquan Huang, Yuan Huang, Xiangping Chen, Xiaocong Zhou et al.ASE 2024 · 2 citations
- Evaluating Generated Commit Messages with Large Language ModelsQunhong Zeng, Yuxia Zhang, Zexiong Ma, Bo Jiang et al.ICSE 2026
- FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message GenerationJinhao Dong, Yiling Lou, Qihao Zhu, Zeyu Sun et al.ICSE 2022 · 50 citations
- Revisiting Learning-based Commit Message GenerationJinhao Dong, Yiling Lou, Dan Hao, Lin TanICSE 2023 · 8 citations
- Delving into Commit-Issue Correlation to Enhance Commit Message Generation ModelsLiran Wang, Xunzhu Tang, Yichen He, Changyu Ren et al.ASE 2023 · 11 citations
