scientific-tdd

Test-driven development for scientific code follows a red-green-refactor workflow: write a failing test first, run it to confirm failure, implement minimal code to pass, run the test to confirm success, then refactor. Use pytest to structure tests, include numerical validation checks for mathematical correctness, and commit with descriptive messages documenting each step.

Scientific code often contains subtle bugs that compromise reproducibility and result reliability. Testing numerical methods and algorithms before deployment catches correctness issues early, validates mathematical assumptions, ensures stability of calculations, and prevents costly errors in research outcomes and publications.

Yes. Pytest integrates seamlessly with numpy for scientific testing. Write tests that validate numerical outputs against expected ranges, check edge cases, and verify stability of mathematical operations. Pytest fixtures can initialize numpy arrays and pytest parametrize can test multiple numerical scenarios efficiently.

Test edge cases, boundary conditions, and mathematical invariants. For algorithms and numerical models, verify outputs match expected behavior, validate numerical stability, check that mathematical properties hold, and confirm results are reproducible. Write tests before implementation to define expected behavior upfront.

Write a failing test that reproduces the bug, then fix the code to make the test pass. This approach confirms the fix works and prevents regression. For numerical bugs, include validation checks that verify the corrected mathematical behavior against known correct outputs or theoretical expectations.

Combine test-driven development with pytest-based testing and numerical validation. Write tests before implementing features, run your full test suite regularly, validate numerical results for mathematical correctness, and document test creation and execution in commit messages to maintain code quality throughout development.