Comprendia：AI增强型代码理解工具
Comprendia: AI-Augmented Code Comprehension
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中文总结 AI 辅助
该研究提出一款名为Comprendia的Eclipse插件，通过集成结构依赖可视化、LLM代码解释、克隆检测及CVE风险叠加功能，为Java程序理解提供支持，在含已知克隆与漏洞的Java项目上验证了其有效性。
中文摘要 AI 辅助
Comprendia是一款Eclipse插件，用于Java程序理解，它将结构依赖可视化与基于大语言模型（LLM）的代码解释集成在同一个交互式图中。该工具基于四大核心模块：（1）支持实时搜索和多种布局的多边类型依赖图；（2）基于图感知被调用方剪枝（Graph-Aware Callee Pruning，GACP）的LLM解释，GACP是一种可审计策略，利用开发者正在浏览的同一图选择相关被调用方；（3）克隆检测叠加层，用于突出显示代码重复并建议提取为父类的重构机会；（4）由指定URL提供支持的CVE风险叠加层。GACP利用图距离、继承崩溃和边类型加权生成提示，这些提示在不同LLM系列间可复现，且可追溯至可见图节点。我们在一个包含已知克隆和漏洞的Java项目上演示了Comprendia，展示了统一的图基础如何在让开发者保持控制权的同时支持代码理解。演示视频：指定URL
英文摘要
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
发表机构
- Idaho State University（爱达荷州立大学）
机构由 AI 辅助整理，请以论文原文为准。
