AI 中文总结
研究提出DebugTracker这一VS Code扩展，用于记录课堂调试的轻量级过程证据，它能分离不同模式跟踪、存储特定事件并导出报告，与语言无关，通过标准机制捕获证据，经多语言调试任务等验证了原型。
AI 中文摘要
调试练习通常根据最终代码和测试结果进行评估，但这些工件隐藏了学生重现失败、形成假设、检查证据、编辑代码和验证修复的方式。我们展示了DebugTracker，这是一个Visual Studio Code扩展，用于记录课堂任务的轻量级调试过程证据。DebugTracker将无指导的评估模式跟踪与有指导的训练模式跟踪分开，存储仅追加的JSONL事件，并导出时间线和Markdown报告以供人工审查。原型记录测试命令、编辑器和调试器元数据、学生检查点、源快照、可选图像证据、人工标签以及可选的人工智能辅助练习反馈。DebugTracker在很大程度上与语言无关：它通过标准的VS Code机制而不是特定于语言的工具来捕获过程证据，尽管调试器证据取决于相关的VS Code语言扩展。我们使用Python、TypeScript和Java中的调试任务、16项自动检查以及涵盖打包VSIX安装和三个操作系统的11个案例的手动试验矩阵对原型进行了验证。
英文摘要
Debugging exercises are often assessed from final code and test outcomes, yet these artifacts hide how students reproduced failures, formed hypotheses, inspected evidence, edited code, and verified fixes. We present DebugTracker, a Visual Studio Code extension that records lightweight debugging-process evidence for classroom tasks. DebugTracker separates uncoached Evaluation Mode traces from coached Training Mode traces, stores append-only JSONL events, and exports timeline and Markdown reports for human review. The prototype records test commands, editor and debugger metadata, student checkpoints, source snapshots, optional image evidence, human labels, and optional AI-assisted practice feedback. DebugTracker is largely language-agnostic: it captures process evidence through standard VS Code mechanisms rather than language-specific tooling, although debugger evidence depends on the relevant VS Code language extension. We validate the prototype with debugging tasks in Python, TypeScript, and Java, 16 automated checks, and an 11-case manual trial matrix spanning packaged VSIX installation and three operating systems.
Comments6 pages. Accepted to the ISSTA 2026 Tool Demonstrations Track; published in the Companion Proceedings of SPLASH Companion '26
