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BLAgent framework enhances file-level bug localization with agentic RAG

By PulseAugur Editorial · [1 sources] ·

Researchers have developed BLAgent, a novel agentic retrieval-augmented generation (RAG) framework designed to improve file-level bug localization in software maintenance. BLAgent integrates code structure-aware encoding, dual-perspective query transformation, and a two-phase agentic reranking process. This approach balances localization accuracy with computational cost, achieving over 78% Top-1 accuracy on SWE-bench Lite with open-source models and over 86% with a closed-source model, while being significantly more cost-effective than existing methods. When integrated into an automated program repair framework, BLAgent has demonstrated an improvement of up to 25% in end-to-end repair success. AI

IMPACT Enhances efficiency and accuracy in software debugging and automated program repair.

RANK_REASON The cluster contains a research paper detailing a novel framework for bug localization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

BLAgent framework enhances file-level bug localization with agentic RAG

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The cluster contains a research paper detailing a novel framework for bug localization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Md Afif Al Mamun, Gias Uddin ·

    BLAgent: Agentic RAG for File-Level Bug Localization

    arXiv:2605.17965v2 Announce Type: replace-cross Abstract: Bug localization remains a key bottleneck for large language model (LLM)-based software maintenance, where accurately identifying faulty code is essential for debugging, root cause analysis, triage, and automated program r…

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