DeepRepoQA: Code Repository Question Answering with Deep Agent Exploration

Abstract:Answering developer questions about a software repository is a critical yet under-explored problem in software engineering. While existing repository understanding methods have advanced the field, they predominantly rely on surface-level code retrieval and lack the ability for deep reasoning over multiple files, complex software architectures, and grounding answers in long-range code dependencies. To address these limitations, we propose DeepRepoQA, a novel question answering (QA) framework for repository-level code understanding. DeepRepoQA builds on an agentic framework where LLM agents find answers through a systematic tree search over the repository structure. A Monte-Carlo Tree Search (MCTS) mechanism is employed to empower agents to dynamically search, navigate, and inspect code, enabling effective multi-hop reasoning over long-range code dependencies. Comprehensive experiments on the SWE-QA benchmark demonstrate substantial performance gains over strong baselines, validating the effectiveness of systematic MCTS-guided exploration for multi-hop repository reasoning.

Software Engineering (cs.SE); Computation and Language (cs.CL); Programming Languages (cs.PL)

arXiv:2608.24221 [cs.SE]

(or arXiv:2608.24221v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2608.24221 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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