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Code Provenance Logger

Code Provenance Logger

HongwonJeong

| (0) | Free
CS1 교육 과정을 위한 코딩 과정 추적 및 분석 도구
Installation
Launch VS Code Quick Open (Ctrl+P), paste the following command, and press enter.
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Code Provenance Logger

Code Provenance Logger is a VS Code extension for CS1 (Introduction to Computer Science) education and research. It logs how students write, modify, execute, and debug code, while preserving privacy by design.

The extension focuses on coding behavior and process, not on storing source code content.

Features

  • Logs coding behavior events:
    • Edit (insert / delete / replace)
    • Save
    • Cursor & selection (debounced)
    • File open / close
    • Program run (task and terminal execution)
    • Diagnostics (errors and warnings)
  • Privacy-preserving by default
    • No raw source code is stored
    • No raw error messages are stored
    • All text content is recorded as SHA-256 hashes only
  • Efficient logging
    • One JSONL file per coding session
    • Hybrid batching (time-based + event-based)
    • Immediate flush on save, close, and run events
  • Research-ready format
    • Structured JSONL logs
    • Batch-level integrity hashes
    • Suitable for large-scale analysis and provenance tracking

Usage

  1. Install the extension using a .vsix file VS Code → Extensions → … → Install from VSIX… or: code --install-extension code-provenance-logger-0.0.1.vsix

  2. Restart VS Code The extension activates automatically on startup.

  3. Code as usual Write, save, and run programs normally.

  4. Find log files at: Windows %APPDATA%\Code\User\globalStorage\HongwonJeong.code-provenance-logger\logs

Logged Data (Summary)

  • Edit features (length, newline count, character classes)
  • Save-time document hash and line count
  • Cursor position and selection ranges
  • File open/close timestamps
  • Run attempts and exit codes (when available)
  • Diagnostic counts (errors, warnings, infos)

Raw code and raw messages are never stored.

Use Cases

  • Distinguishing typing-based coding from copy-and-paste behavior
  • Analyzing error–fix–run cycles in CS1 assignments
  • Studying students’ coding strategies and progress
  • Building datasets for educational data mining and AI feedback

Notes

  • Intended for educational and research purposes
  • User consent is recommended before deployment
  • No personally identifiable information is collected

License

MIT

Author

Hongwon Jeong Hanyang University beatsbywoni@hanyang.ac.kr

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