用大模型+结构化视图帮开发者高效理解复杂代码库
AI-Guided Exploration of Large-Scale Codebases
- 结合逆向工程与大模型，实现意图感知的交互式代码探索
- 通过用户查询和操作模式，动态引导代码导航与理解
- 适合需要快速掌握大型代码系统的开发人员和团队
理解大规模复杂软件系统是开发者的重大挑战，他们需花费大量时间进行程序分析。传统工具如静态可视化和逆向工程虽提供结构信息，但缺乏互动性、适应性及上下文整合能力。近年来大语言模型（LLMs）为改进代码探索流程带来新机遇，但其缺乏事实依据且难以融入结构化视图，限制了实际效果。本文提出一种混合方法：将确定性的逆向工程与基于大模型的意图感知视觉探索相结合。所提系统融合了基于UML的可视化、动态用户界面、历史上下文与协作功能，构建了一个自适应的代码理解工具。通过解析用户查询和交互行为，大模型辅助开发者更高效地导航和理解复杂代码库。针对Java的原型实现验证了该方法的可行性。未来工作包括开展实证评估、扩展至多语言系统，并探索以图形界面驱动的大模型交互模式。本研究为契合开发者认知与协作流程的智能交互环境奠定了基础。
原文摘要 · Abstract (English)
Understanding large-scale, complex software systems is a major challenge for developers, who spend a significant portion of their time on program comprehension. Traditional tools such as static visualizations and reverse engineering techniques provide structural insights but often lack interactivity, adaptability, and integration with contextual information. Recent advancements in large language models (LLMs) offer new opportunities to enhance code exploration workflows, yet their lack of grounding and integration with structured views limits their effectiveness. This work introduces a hybrid approach that integrates deterministic reverse engineering with LLM-guided, intent-aware visual exploration. The proposed system combines UML-based visualization, dynamic user interfaces, historical context, and collaborative features into an adaptive tool for code comprehension. By interpreting user queries and interaction patterns, the LLM helps developers navigate and understand complex codebases more effectively. A prototype implementation for Java demonstrates the feasibility of this approach. Future work includes empirical evaluation, scaling to polyglot systems, and exploring GUI-driven LLM interaction models. This research lays the groundwork for intelligent, interactive environments that align with developer cognition and collaborative workflows.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。