[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83008-en":3,"doc-seo-83008-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83008,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","DebugTracker: Lightweight Process Evidence for Classroom Debugging","Debugging exercises are often graded using only final code and test outcomes, even though these artifacts conceal how students reproduced failures, formed hypotheses, inspected evidence, edited code, and validated fixes. DebugTracker presents a Visual Studio Code extension that captures lightweight debugging-process evidence for classroom tasks. It distinguishes uncoached Evaluation Mode traces from coached Training Mode traces, records append-only JSONL events, and exports timeline and Markdown reports for instructor review.","DebugTracker: Lightweight Process Evidence for Classroom  \nDebugging  \nJiatong Liu  \nMonash University Melbourne, Australia [jliu0455@student.monash.edu](jliu0455@student.monash.edu)  \nXue Yao  \nMonash University Melbourne, Australia [xyao0028@student.monash.edu](xyao0028@student.monash.edu)  \nZehua Zhang  \nMonash University Melbourne, Australia [zzha0610@student.monash.edu](zzha0610@student.monash.edu)  \nYongqiang Tian  \nMonash University Melbourne, Australia [yongqiang.tian@monash.edu](yongqiang.tian@monash.edu)  \narXiv :2607 .0587 1v 1 [ cs . SE] 7 Jul 2026  \nAbstract  \nDebugging 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.  \nCCS Concepts  \n• Software and its engineering → Software testing and debugging; • Social and professional topics → Software engineering education.  \nKeywords  \ndebugging, software testing education, VS Code extension, tool demonstration  \n1 Introduction  \nDebugging is a process of observing failures, asking questions, inspecting state, testing hypotheses, and validating repairs [9, 12] . It is also one of the hardest skills for students to learn because the reasoning that separates a systematic investigation from lucky guessing happens between the failing test and the final patch. Classroom assessment, however, commonly sees only that final patch, the final test result, or a short written note. The intermediate steps—reproducing the failure, narrowing it to a suspect location, forming a hypothesis, and checking that the repair fixes the observed symptom—leave no durable record once the session ends.  \nThis creates a process-visibility gap in assessment. Viewed from the student’s side, a debugging exercise unfolds as the rich, multistep process just described. Viewed from the instructor’s or examiner’s side, however, the available artifact is often only the submitted code plus a pass/fail test result. Consider two students who both submit a passing solution: one may have followed an evidencebased debugging process, while the other may have edited and tested repeatedly until the suite turned green. The final code can look identical even when the underlying debugging competence differs. When the goal of the exercise is to teach a process, feedback that ignores the process misses what the exercise was meant to assess.  \nRicher alternatives exist but fit the classroom poorly. Screen recording and live observation capture the process faithfully, yet they do not scale to large cohorts, are intrusive, and collect far more data—faces, unrelated windows, keystrokes—than a review task requires. Commit history is too coarse and is easily rewritten, so it rarely reflects the moment-to-moment investigation. Automated debugging and fault-localization tools target the act of finding bugs, not the assessment of how a learner did so, and prior work cautions that such automation does not straightforwardly help programmers in practice [7]. Classroom assessment the","cbCaiajizveK4wcA","https://ap.wps.com/l/cbCaiajizveK4wcA","pdf",1436940,2,1,5,"English","en",105,"# Introduction\n## Process-visibility gap in debugging assessment\n## Limitations of existing alternatives\n## DebugTracker goals and contributions\n## Workflow and modes","[{\"question\":\"What problem does DebugTracker address in classroom debugging assessment?\",\"answer\":\"It addresses the process-visibility gap where grading often relies only on final patches and pass/fail results, leaving students’ intermediate debugging reasoning undocumented.\"},{\"question\":\"How does DebugTracker support different learning/assessment modes?\",\"answer\":\"It uses two mutually exclusive modes: Evaluation Mode records uncoached traces with hints and prompts disabled, while Training Mode enables coached practice prompts and optional AI feedback with restrictions on direct patches.\"},{\"question\":\"What types of evidence does DebugTracker record for later review?\",\"answer\":\"It records test commands, editor and debugger metadata, student checkpoints, source snapshots, optional image evidence, human labels, and optional AI-assisted practice feedback, then exports timeline and Markdown 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