DebugTracker: Lightweight Process Evidence for Classroom Debugging

2026-06-24 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

DebugTracker, a Visual Studio Code extension released in 2026, records lightweight debugging-process evidence for classroom tasks, addressing the gap where traditional assessment only considers final code and test outcomes. It operates in two distinct modes: uncoached Evaluation Mode for formal assessment and coached Training Mode for practice, which can incorporate optional OpenAI-compatible AI feedback. The tool captures critical events like test commands, editor/debugger metadata, student checkpoints, source snapshots, and human labels, storing them as append-only JSONL events. DebugTracker then exports these into reviewable timeline and Markdown reports. Designed to be largely language-agnostic, it has been validated with debugging tasks in Python, TypeScript, and Java, passing 16 automated checks and an 11-case manual trial matrix across three operating systems. The extension targets VS Code 1.88 or newer and is available on GitHub.

Key takeaway

For computer science instructors assessing debugging skills, DebugTracker offers a scalable solution to observe student processes beyond final code. You can use its Evaluation Mode to capture structured evidence like hypotheses and edits, providing a clear narrative for feedback. This allows you to identify systematic investigation versus trial-and-error, improving the specificity and fairness of your grading. Consider integrating it for process-oriented assessment.

Key insights

DebugTracker provides lightweight, structured evidence of student debugging processes, bridging the gap between final code and actual skill.

Principles

• Assess debugging process, not just outcome.
• Separate assessment from coached practice.
• Capture task-relevant metadata, not full replay.

Method

DebugTracker records VS Code events (tests, edits, breakpoints, checkpoints) into append-only JSONL logs. These logs generate timeline and Markdown reports for instructor review.

In practice

• Use Evaluation Mode for uncoached student assessment.
• Employ Training Mode for guided practice with AI feedback.
• Review timeline and Markdown reports for process insights.

Topics

• Debugging Education
• VS Code Extension
• Programming Process Analytics
• Software Testing Education
• Formative Assessment
• AI-assisted Feedback

Code references

• t3-research/DebugTracker

Best for: AI Scientist, AI Student, Software Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.