Meta-RAG on Large Codebases Using Code Summarization

• URL: http://arxiv.org/abs/2508.02611v1
• Date: Mon, 04 Aug 2025 17:01:10 GMT
• Title: Meta-RAG on Large Codebases Using Code Summarization
• Authors: Vali Tawosia, Salwa Alamir, Xiaomo Liu, Manuela Veloso,
• Abstract summary: Large Language Model (LLM) systems have been at the forefront of applied Artificial Intelligence (AI) research in a multitude of domains.<n>We propose a multi-agent system to localize bugs in large pre-existings using information retrieval and LLMs.<n>Our system introduces a novel Retrieval Augmented Generation (RAG) approach, Meta-RAG, where we utilize summaries to condenses by an average of 79.8%, into a compact, structured, natural language representation.
• Score: 11.415083231118142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Model (LLM) systems have been at the forefront of applied Artificial Intelligence (AI) research in a multitude of domains. One such domain is software development, where researchers have pushed the automation of a number of code tasks through LLM agents. Software development is a complex ecosystem, that stretches far beyond code implementation and well into the realm of code maintenance. In this paper, we propose a multi-agent system to localize bugs in large pre-existing codebases using information retrieval and LLMs. Our system introduces a novel Retrieval Augmented Generation (RAG) approach, Meta-RAG, where we utilize summaries to condense codebases by an average of 79.8\%, into a compact, structured, natural language representation. We then use an LLM agent to determine which parts of the codebase are critical for bug resolution, i.e. bug localization. We demonstrate the usefulness of Meta-RAG through evaluation with the SWE-bench Lite dataset. Meta-RAG scores 84.67 % and 53.0 % for file-level and function-level correct localization rates, respectively, achieving state-of-the-art performance.

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