English(EN) Accepted but Wrong: Silent Failures of LLM Code Repairs after API Migrations

研究发现LLM代码修复可能存在静默错误

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

一项发表在arXiv上的新研究表明，大型语言模型（LLM）生成的代码修复可能看起来正确，但实际上功能错误，而API迁移加剧了这一问题。研究人员发现，标准的测试方法通常无法捕捉到这些静默错误。虽然特定的输入策略和盲目AI审计提高了检测率，但仍有相当一部分不正确的代码通过了测试。 AI

影响强调了在生产环境中，对LLM生成的代码需要更强大的测试和验证方法。

排序理由该集群包含一篇详细介绍LLM代码修复研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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arXiv cs.AI TIER_1 English(EN) · Xiaobo Chen, Genliang Zhu, Ziyuan Wang, Chu Wang · 2026-11-06 05:00

接受但错误：API迁移后LLM代码修复的沉默失败

arXiv:2611.08269v1 Announce Type: cross Abstract: After a runtime or API migration, an LLM-repaired procedure can run cleanly yet return wrong results, and a release check on typical test inputs ships it. We report a preregistered study of 73 tasks covering 60 documented changes …