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English(EN) CuraLoc: Repository-Level Fault Localization via Planned Code Inspection

CuraLoc 计划代码检查以提高故障定位效率

研究人员开发了 CuraLoc,一种用于仓库级故障定位的新颖方法,该方法在检索源代码之前就计划代码检查。这种方法与使用迭代模型调用的现有方法不同,从而减少了令牌使用量并提高了效率。CuraLoc 首先识别候选文件并生成符号描述,然后在单个规划步骤中联合选择起始符号、代码关系和潜在的编辑。该系统在 SWE-Bench Verified 和 SWE-bench Lite 数据集上展示了更高的准确性,优于其他方法,并显示出与 GPT-5.2 和 GPT-6 Astra 等不同模型骨干的灵活性。 AI

影响 这项研究可能为软件开发人员带来更高效、更准确的调试工具,从而缩短开发时间和降低成本。

排序理由 该集群描述了一篇学术论文中提出的一种新颖方法,重点关注软件开发中故障定位的特定技术方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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CuraLoc 计划代码检查以提高故障定位效率

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该集群描述了一篇学术论文中提出的一种新颖方法,重点关注软件开发中故障定位的特定技术方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sangeeta Warrier, Anush Sankaran, Ryen White ·

    CuraLoc:通过计划性代码审查实现存储库级故障定位

    arXiv:2610.31809v1 Announce Type: new Abstract: A bug report describes what went wrong, but rarely where the code should change. Existing repository-level localization methods often let language models iteratively decide what code to inspect next, requiring repeated model calls a…