arXiv：cs.AI· Jinfeng Jiang, Dongsun Kim, Dayi Lin, Zhou Yang·· 3 小时前
RucTangle：让编码智能体的提交拆分保持代码可运行
Runnable Commit Untangling for Coding Agents
AI 导读
论文提出 RucTangle，首个在拆分提交时保证每次提交后代码仍可运行的智能体方法，并配套 TangleEval 评估框架，量化拆分后的提交历史如何帮助编码智能体修复 bug。
正文
Abstract:Coding agents produce large, tangled patches that mix multiple development purposes, making the code hard to review and maintain. Commit untangling offers the promise of organizing such large patches into untangled, manageable commits. This paper emphasizes two important limitations in existing commit untangling studies. First, they do not consider that untangled commits are ordered and should leave the code runnable. In practice, maintainers are unlikely to accept commits that prevent the code from running. Second, existing studies claim that commit untangling helps software maintenance. However, they conduct syntactic comparisons between the untangled commits and developers' original commits without directly showing the claimed maintenance benefits. To address these gaps, this paper makes two novel contributions: (1) RucTangle, the first agentic method that untangles commits while keeping the code runnable after each commit; and (2) TangleEval, the first evaluation framework that quantifies how untangled, manageable commit histories help coding agents repair bugs. We compare RucTangle against four untangling methods on 131 agent-generated patches. All histories produced by RucTangle are runnable, while baselines produce 20.6%-37.4% unrunnable commit histories. We further collect 453 agent-generated patches that introduce regressions (i.e., causing previously passing tests to fail) and ask two other coding agents to repair regressions. Augmenting agent context with RucTangle-produced histories yields 5.2% absolute improvement in pass@1. We also analyze agent trajectories to learn how they use untangled commits to navigate and fix bugs. Our findings demonstrate the value of adopting established software engineering practices in the era of coding agents, which broaden the future research agenda: how can agents actively use software history to make better development decisions?
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.11593 [cs.SE] |
| (or arXiv:2610.11593v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11593 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jinfeng Jiang [view email]
[v1]
Thu, 8 Oct 2026 09:42:27 UTC (659 KB)
来源：arXiv：cs.AI · arxiv.org