arXiv cs.AI· Rishi Raj, Aashray Tandon, Nikolay Matyunin, Jibesh Patra·· 13 小时前AI 评分24
ComBeautify：用代码变更摘要生成 Conventional Commits 提交信息
ComBeautify: Leveraging Code Change Summaries for Conventional Commit Generation
AI 导读
ComBeautify 通过 LLM 对 Git diff 做保留上下文的摘要，自动生成符合 Conventional Commits 规范的提交信息，并从历史提交中学习项目专属的类别定义。
正文 · 原文
Abstract:Modern software development relies on version control systems such as Git to track changes and facilitate collaboration. While commit messages are crucial for conveying developer intent, they do not affect program functionality; as a result, developers often lack motivation to write meaningful ones. The Conventional Commits Specification (CCS) addresses this with a structured format and guidelines that help developers express the intent of a change, improving clarity and enabling automation. Yet, developers frequently struggle to express intent while adhering to the format. In particular, they may misclassify commits due to the large number of possible categories or the lack of project-specific guidance. We introduce ComBeautify, an approach that automatically generates CCS-compliant commit messages. Our hybrid approach leverages context-preserving summarization of Git diffs with large language models (LLMs) to improve commit classification accuracy under CCS. Rather than relying on manually curated category definitions, we learn them from historical commits. We also propose a method for automatically generating the CCS description component.
Our empirical evaluation across 13 languages, 6,500 open-source repositories, and over 20 million commits provides long-term insights into CCS adoption, revealing increasing compliance. Our evaluation on twenty popular repositories reveals the limitations of approaches guided by generic category definitions, and shows that ComBeautify, when trained on project-specific data, outperforms baselines by up to 3x. Additionally, using a dataset of 2,000 manually curated commits, we validate our summarization approach, achieving up to 95.25% accuracy and surpassing baselines. These findings emphasize the need for adaptable, context-aware AI methods for commit classification and CCS-compliant commit message generation.
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.34374 [cs.SE] |
| (or arXiv:2610.34374v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34374 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Nikolay Matyunin [view email]
[v1]
Sun, 25 Oct 2026 15:31:39 UTC (131 KB)
来源：arXiv cs.AI · arxiv.org