用交互图可视化代码依赖，结合大模型解释，提升Java代码理解效率。
Comprendia: AI-Augmented Code Comprehension
- 构建多类型边的动态依赖图，支持搜索与多种布局。
- 通过图感知调用筛选生成可追溯的大模型解释。
- 集成代码重复检测与漏洞风险提示，辅助重构与安全分析。
Comprendia 是一个 Eclipse 插件，将结构化依赖可视化与基于大语言模型（LLM）的代码解释整合到共享的交互式图中，用于提升 Java 程序理解效率。该工具基于四大支柱：(1) 支持实时搜索和多种布局的多边类型依赖图；(2) 基于图感知调用剪枝（GACP）的大模型解释，通过图距离、继承折叠和边类型加权选择相关被调用方，确保解释可复现且可追溯至可视节点；(3) 代码克隆检测叠加层，识别重复代码并建议提取为父类重构；(4) 基于 OSV.dev 的漏洞风险叠加层。我们在一个包含已知代码克隆和漏洞的 Java 项目上演示了 Comprendia，证明统一图结构能有效支持代码理解，同时保持开发者主导权。视频演示：https://youtu.be/1wlh_RYehzA
原文摘要 · Abstract (English)
Comprendia is an Eclipse plugin that integrates structural dependency visualization with LLM-powered code explanation on a shared interactive graph for Java program comprehension. The tool rests on four pillars: (1) a multi-edge-type dependency graph with live search and multiple layouts; (2) LLM explanations grounded in Graph-Aware Callee Pruning (GACP), an auditable strategy that selects relevant callees using the same graph the developer navigates; (3) a clone-detection overlay that highlights duplication and suggests extract-to-parent refactoring opportunities; and (4) a CVE risk overlay powered by OSV.dev. GACP uses graph distance, inheritance collapse, and edge-type weighting to produce prompts that are reproducible across LLM families and traceable to visible graph nodes. We demonstrate Comprendia on a Java project containing known clones and vulnerabilities, showing how the unified graph substrate supports comprehension while keeping the developer in control. Screencast: https://youtu.be/1wlh_RYehzA
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