记录开发者与AI编程助手的完整交互过程，支持回放与分析。
RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions
- 被动记录代码编辑和聊天对话，不干扰开发流程
- 整合2034条提示和8239次代码修改，形成统一时间线
- 适用于教学研究，可分析人机协作模式与依赖程度
理解开发者如何使用AI编程助手，不能仅依赖聊天记录或git历史；必须重建完整上下文：哪个提示引发哪次修改、开发者尝试过哪些被放弃的方案，以及策略随时间的演变。我们提出RECAP（Replay and Examine Captured AI Programming）——一个开源平台，(1) 无侵入式地在VS Code中记录AI聊天会话和细粒度代码编辑；(2) 将其合并为统一时间线，支持交互式回放；(3) 提供可扩展的分析层，包含行为分类与AI依赖度测量等模块。该平台在高校软件工程课程中部署，共捕获41名学生在多周项目中的2034条提示和8239次代码修改。结果表明，平台通过数据关联与回放功能，实现了单一数据源无法支持的开发者-AI交互模式分析。
原文摘要 · Abstract (English)
Understanding how developers interact with AI coding assistants requires more than chat logs or git histories in isolation; it requires reconstructing the full context: which prompt led to which edit, what the developer tried and discarded, and how their strategy evolved over time. We present RECAP (Replay and Examine Captured AI Programming), an open-source platform that (1) passively records AI chat sessions and fine-grained code edits inside VS Code without disrupting the developer's workflow, (2) merges them into a unified timeline for interactive session replay, and (3) exposes an extensible analysis layer, with example modules for behavioral classification and AI reliance measurement. Deployed in a university software engineering course, RECAP captured 2,034 prompts and 8,239 code edits from 41 students across a multi-week project. We demonstrate how the platform's linked data and replay capabilities enable analyses of developer-AI interaction patterns that no single data source could support.
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。