Chunying Zhou, Gong Chen, Xiaoyuan Xie, Yi Song, Yuxuan Yang, Peng He · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.25303 · cited by 0 works (OpenAlex) · open-access PDF
承诺消息提供代码变化的记录,并帮助开发人员理解和维护软件。 为了减少编写它们所涉及的手动努力,承诺消息生成(CMG)方法会自动生成承诺消息,以自然语言描述代码变更。 现有的CMG方法通常将CMG作为一个独立的任务来处理,并优化生成的消息,主要是为了与开发人员撰写的引用相似。 然而,这个目标可能无法正确地反映生成的承诺消息的实际用途。 这种局限性在当前AI辅助软件工程工作流程中具有重要性,其中承诺消息被用作共享信息来服务于不同的软件工程活动。 为了填补这一差距,我们建议TraceMsg,该目标使用问题承诺追踪性,是一个代表性的软件工程活动,以指导承诺消息生成。
Machine translation (Meta M2M100); the English original follows.
Commit messages provide records of code changes and help developers understand and maintain software. To reduce the manual effort involved in writing them, commit message generation (CMG) approaches automatically generate commit messages that describe code changes in natural language. Existing CMG approaches generally treat CMG as a standalone task and optimize generated messages primarily for similarity to developer-written references. However, this objective may not properly reflect the practical usefulness of generated commit messages. This limitation matters in current AI-assisted software engineering workflows, where commit messages are used as shared information to serve different software engineering activities. To fill this gap, we propose TraceMsg, which uses issue-commit traceability, a representative software engineering activity, to guide commit message generation. TraceMsg is a collaborative multi-agent framework that coordinates message generation, traceability reasoning, and feedback-guided refinement, allowing generated messages to serve traceability while using reasoning feedback to guide issue-conditioned refinement. We evaluate TraceMsg on six open-source projects. The experimental results show that TraceMsg achieves an average Hit@1 of 0.893 and an MRR of 0.911, improving Hit@1 and MRR by 13.16% and 38.75%, respectively, over the best baseline for each metric.
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Topics: Commit, Traceability, Computer science, Software, Task (project management), Software engineering
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