Loc2Repair: A Framework for Evaluating the Impact of File-Level Issue Localization in Repo-Level LLM Repair
arXiv:2606.30963v1 Announce Type: cross Abstract: Repository-grounded automated repair is often reported as a single end-to-end capability, which hides distinct failure modes such as poor file targeting, incorrect patch synthesis, and failed iterative debugging. We present Loc2Repair, a modular evaluation framework for controlled analysis of repository-grounded repair pipelines, and use it to isolate file-level issue localization as an upstream variable. Loc2Repair decouples localization and repair under a shared runtime, artifact schema, and evaluation harness, allowing researchers to combine
The proliferation of Large Language Models (LLMs) and their application in code repair necessitates robust evaluation frameworks to understand and improve their real-world efficacy.
This framework offers a granular approach to evaluating LLM-based code repair, moving beyond 'end-to-end' metrics to identify specific failure modes like poor file targeting, which is crucial for advancing AI agent capabilities in software development.
The ability to decouple and analyze distinct components of LLM-based repair pipelines will lead to more targeted research and development, potentially accelerating the deployment of reliable autonomous code repair agents.
- · AI researchers
- · Software developers
- · AI agent developers
- · DevOps platforms
- · Unoptimized LLM-based repair solutions
- · Manual debugging processes
Improved understanding and performance of LLM-based automated code repair systems.
Faster, more reliable software development cycles due to advanced AI assistance in debugging and repair.
Increased automation of software engineering tasks, potentially shifting job roles and demanding new skills in managing AI-driven development.
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Read at arXiv cs.AI