issue-resolution.html 关键词 issue resolution 的长期追踪 RSS，汇总历史命中文献。 zh-CN Sun, 28 Jun 2026 05:24:06 +0000 ../papers/arxiv-f7ba1bc50aef.html https://arxiv.org/abs/2606.25514v1#2026-06-25#issue-resolution Thu, 25 Jun 2026 13:11:21 +0800 Resolving issues with ambiguous and incomplete descriptions, particularly concerning complex bugs, requires a sophisticated, long-horizon workflow. Agents must navigate codebases to locate the root cause, reproduce the failure, implement a fix, and validate the resulting patch. Inefficient context management, thereby, can lead to rapid context degradation and context poisoning, preventing successful resolution. We propose icat-agent, a decentralized, multi-agent scaffolding that replaces shared… ../papers/arxiv-59528d739abc.html https://arxiv.org/abs/2606.20243v1#2026-06-19#issue-resolution Fri, 19 Jun 2026 14:26:15 +0800 We present Phoenix, a multi-agent LLM system that resolves GitHub issues from triage through pull-request creation, combining seven layered safety controls with a baseline-aware test evaluation strategy. Phoenix decomposes the work across six specialized agents. Planner, reproducer, coder, tester, failure analyst and Pull Request (PR) agent, all coordinated by a label-based GitHub webhook state machine. Every change is checked against a baseline test run before a pull request is opened. On a 24… ../papers/arxiv-9dc3da77bc41.html https://arxiv.org/abs/2605.22526v1#2026-05-22#issue-resolution Fri, 22 May 2026 13:08:19 +0800 Recent advances in coding agents have shown remarkable progress in software issue resolution. In practice, real-world issues are typically bug fixes or feature requests in which human developers naturally incorporate refactoring as part of the resolution process, resulting in tangled refactoring. Since LLMs are trained on large-scale open-source repositories, coding agents may inherit such behaviors. In this paper, we conduct an empirical study on Multi-SWE-bench, analyzing 3,691 valid patches… ../papers/arxiv-ff63d48f53a7.html https://arxiv.org/abs/2605.14415#2026-05-15#issue-resolution Fri, 15 May 2026 14:57:29 +0800 Coding agents powered by large language models are increasingly expected to perform realistic software maintenance tasks beyond isolated issue resolution. Existing benchmarks have shifted toward realistic software evolution, but they rarely capture continuous maintenance at the granularity of package releases, where changes are bundled, shipped, and inherited by subsequent versions. We present SWE-Chain, a benchmark for evaluating agents on chained release-level package upgrades, where each tra…