DeepRepoQA: Code Repository Question Answering with Deep Agent Exploration

• URL: http://arxiv.org/abs/2608.24221v1
• Date: Tue, 25 Aug 2026 08:26:31 GMT
• Title: DeepRepoQA: Code Repository Question Answering with Deep Agent Exploration
• Abstract summary: DeepRepoQA is a novelQA framework for repository-level code understanding.<n>A Monte-Carlo Tree Search (MCTS) mechanism is employed to empower agents to dynamically search, navigate, and inspect code.
• Score: 10.655923413185498
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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