A rough work in progress roadmap for docstub during its early development phase:

v0.3 (Alpha)

Minimum threshold to create stubs for scikit-image.

• Add basic CI tests
  • Add basic CI configuration #8
• Errors and warnings point to parsed source if possible
  • Point to precise line in parsed source for parsing problems #10
• Cache static analysis
  • Cache results of static analysis #15
• Support module attributes, class attributes, and type aliases
  • Support attributes and type aliases #18
• CI testing with mypy
  • Use mypy.stubtest in CI #25
  • Check test suite with mypy #27
• Report statistics at the end: error count, file count, runtime, ...
  • Basic stats in 0b048f3
• Minimal documentation
  • Add minimal documentation #24

v0.x

Typing support

• Direct support for dataclasses
  • Add direct support for dataclasses #26
• Support generator functions and "Yields" section
  • Support Yields section and Generator functions #29
• Support callables with signatures, e.g. callable(int, str) -> float
• Direct support for Enums
• Support Generics in docstrings

Testing & correctness

• CI testing with other type checkers (basedpyright, Pytype, Pyre, ...)
  • Check tests/ with basedpyright in CI in "standard" mode #50
• "Dogfooding" -- publish and distribute stubs for docstub

Adoption

• Evaluate on scikit-image
  • Generate typing stubs with docstub scikit-image/scikit-image#7546
  • https://github.com/scikit-image/scikit-image/labels/%3Ajigsaw%3A%20typing
• Evaluate on networkx
• Pin upper limit for allowed errors with "ratchet"
  • Add --allow-errors command line option #32
• optional grouping of reported errors
  • Add --group-errors option #30
• Support checking & transforming a single doctype for testing & debugging in CLI
• Add website with documentation

Performance

• Explore parallelization
• Profile & optimize
  • Evaluate using Lark's LALR(1) parser
• Cache transformed doctypes
• Cache created stubs

Other

• Add support for grammar & transformation extensions
• Support docstring styles other than NumPyDoc (already supported)
• Add automated release infrastructure
• Provide plugin-like hooks to extend functionality
  • E.g. supporting things like leverage sphinx config info to improve parsing #22

Long-term / v1.0

• Actively used by major projects
• Do runtime analysis to find types?
• Support inline type annotations?
• Inline type annotations (e.g., see libcst's apply_type_annotations)
• Integration with common CI platforms, e.g. see basedpyright's CI integration