Meta-RAG on Large Codebases Using Code Summarization

Vali Tawosi; Salwa Alamir; Xiaomo Liu; Manuela Veloso

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arXiv · 2508.02611

Meta-RAG on Large Codebases Using Code Summarization

Vali Tawosi·Salwa Alamir·Xiaomo Liu·Manuela Veloso

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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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Vali Tawosi, Salwa Alamir, Xiaomo Liu, Manuela Veloso. 2025-08-04. Meta-RAG on Large Codebases Using Code Summarization. https://arxiv.org/abs/2508.02611

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