rb-tdd-scientific-code

Test-driven development for numerical simulation code enforces strict validation of conservation laws, dimensional consistency, and tolerances to prevent silent drift. It ensures physical consistency across code changes by verifying mathematical invariants.

Stochastic validation for probabilistic models ensures distributional correctness rather than relying on single-seed flakiness. It validates statistical properties and distributional outcomes across multiple simulation runs.

Benchmark regression tests for numerical solvers verify that every code change maintains physical consistency and passes rigorous benchmark fixtures. This workflow ensures high-fidelity verification of invariants across coupled model pipelines.

Test-driven development for reproducible research satisfies strict requirements for reproducible research and dimensional consistency. It applies test-driven workflows to scientific codebases requiring high-fidelity verification of invariants and tolerances.

Coupled model pipelines require rigorous integration testing to prevent silent failures in scientific computing. Enforcing strict validation of units, conservation laws, and numerical tolerances prevents drift that isolated unit tests often miss.