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Development Plan

Project Goals

debug-dojo aims to provide a unified, “zen” debugging experience for Python developers. By abstracting the differences between various backend debuggers (pdb, ipdb, pudb, debugpy) and injecting useful runtime helpers (p, c, i, b), it allows developers to focus on the problem at hand rather than the toolchain configuration.

Roadmap

The development roadmap is driven by GitHub issues. Below is the prioritized list of planned work.

Features & Enhancements

  • [#89] Zen Notifications: System notifications for breakpoints and long-running tasks.
  • [#92] Dojo Belts (Gamification): Local stats tracking for usage (bugs crushed).
  • [#2] Specialized Comparers: Improve the c() helper to provide clearer, structural diffs for list and dict types.
  • [#88] Specialized Object Inspectors: Extend inspection tools for specialized objects (Pandas, NumPy, Pydantic).
  • [#90] Mini-Profiler: Lightweight profiling option wrapping cProfile.
  • [#4] PuDB Breakpoint Management: Add functionality to manage pudb’s saved breakpoints via tool configuration.
  • [#91] Robust Crash Handler: Unified interactive crash handler (“Debug it? [Y/n]”).
  • [#85] Enhanced UI with Rich: Leverage rich more extensively for interactive inspection tools during debugging sessions (e.g. better object inspectors, panels).
  • [#33] Direct IPython Integration: Refactor the ipdb backend to potentially use IPython’s debugger directly.
  • [#87] Remote Debugging: Simplify the setup for remote debugging scenarios using debugpy.
  • [#86] Plugin System: Design and implement a plugin system to allow users to define their own helper functions or integrations.

Maintenance & Stability

  • [#82] Maintenance: Dependency Updates: Keep dependencies updated, specifically ensuring compatibility with new versions of supported debuggers.
  • [#83] Stability: Environment Injection Tests: Improve test coverage for edge cases in environment injection logic (_installers.py).
  • [#35] Coverage Enforcement: Integrate coverage reporting into the CI pipeline to ensure code quality.

Documentation & Infrastructure

  • [#84] Documentation: Advanced Usage: Expand the documentation to include more examples of advanced configuration and usage scenarios.
  • [#19] Automated Publishing Workflow: Verify and refine the GitHub Actions for publishing to PyPI and deploying GitHub Pages.