# DeepRepoQA: Code Repository Question Answering with Deep Agent Exploration

arXiv: https://arxiv.org/abs/2608.24221 · HTML: https://mlsanity.com/paper/2608.24221/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 20 of 20)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation](https://mlsanity.com/paper/2303.12570/) (2023)
- [CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges](https://mlsanity.com/paper/2401.07339/) (2024)
- [RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph](https://mlsanity.com/paper/2410.14684/) (2024)
- [CoSQA: 20,000+ Web Queries for Code Search and Question Answering](https://mlsanity.com/paper/2105.13239/) (2021)
- [SWE-Exp: Experience-Driven Software Issue Resolution](https://mlsanity.com/paper/2507.23361/) (2025)
- [SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution](https://mlsanity.com/paper/2507.23348/) (2025)
- [From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging](https://mlsanity.com/paper/2410.01215/) (2024)
- [LongCodeZip: Compress Long Context for Code Language Models](https://mlsanity.com/paper/2510.00446/) (2025)
- [SWE-QA: Can Language Models Answer Repository-level Code Questions?](https://mlsanity.com/paper/2509.14635/) (2025)
- [CodeQA: A Question Answering Dataset for Source Code Comprehension](https://mlsanity.com/paper/2109.08365/) (2021)
- [EvoC2Rust: A Skeleton-guided Framework for Project-Level C-to-Rust Translation](https://mlsanity.com/paper/2508.04295/) (2025)
- [CoReQA: Uncovering Potentials of Language Models in Code Repository Question Answering](https://mlsanity.com/paper/2501.03447/) (2025)
- [ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search](https://mlsanity.com/paper/2403.16702/) (2024)
- [CodeQueries: A Dataset of Semantic Queries over Code](https://mlsanity.com/paper/2209.08372/) (2022)
- [CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Course](https://mlsanity.com/paper/2210.14494/) (2022)
- [Leveraging Large Language Models in Code Question Answering: Baselines and Issues](https://mlsanity.com/paper/2411.03012/) (2024)
- [SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring](https://mlsanity.com/paper/2608.09802/) (2026)
- [A Neural Question Answering System for Basic Questions about Subroutines](https://mlsanity.com/paper/2101.03999/) (2021)

## Abstract (arXiv)

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.