Meta-RAG on Large Codebases Using Code Summarization
This addresses the problem of code maintenance for software developers by improving bug localization efficiency, though it is incremental as it builds on existing RAG and LLM methods.
The paper tackles bug localization in large codebases by proposing Meta-RAG, a multi-agent system that uses code summarization to condense codebases by 79.8% and achieves state-of-the-art performance with 84.67% file-level and 53.0% function-level correct localization rates on the SWE-bench Lite dataset.
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.