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CuraLoc plans code inspections to improve fault localization efficiency

Researchers have developed CuraLoc, a novel method for repository-level fault localization that plans code inspections before source retrieval. This approach contrasts with existing methods that use iterative model calls, thereby reducing token usage and improving efficiency. CuraLoc first identifies candidate files and generates symbolic descriptions, then jointly selects starting symbols, code relationships, and potential edits in a single planning step. The system demonstrated improved accuracy on SWE-Bench Verified and SWE-bench Lite datasets, outperforming other methods and showing flexibility with different model backbones like GPT-5.2 and GPT-6 Astra. AI

IMPACT This research could lead to more efficient and accurate debugging tools for software developers, reducing development time and costs.

RANK_REASON The cluster describes a novel method presented in an academic paper, focusing on a specific technical approach to fault localization in software development. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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CuraLoc plans code inspections to improve fault localization efficiency

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The cluster describes a novel method presented in an academic paper, focusing on a specific technical approach to fault localization in software development. [lever_c_demoted from research: ic=1 ai…
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COVERAGE [1]

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

    CuraLoc: Repository-Level Fault Localization via Planned Code Inspection

    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…