Skip to content

Test Strategy

A useful test strategy names what evidence is needed and why shallow coverage is not enough.

For bijux-proteomics-intelligence, the test story should show how recommendation logic remains explainable under ambiguity, contradiction, and changed outcome paths.

Strategy Model

flowchart TB
    inputs["evidence and policy inputs"]
    evaluators["evaluator and policy tests"]
    ambiguity["ambiguity and contradiction cases"]
    outputs["report and outcome proof"]
    release["release confidence"]

    inputs --> evaluators
    evaluators --> ambiguity
    ambiguity --> outputs
    outputs --> release

The point is not simply to prove that scores move. The strategy has to show why a recommendation changed and whether that explanation survives all the way to the published outputs.

Review Rules

  • favor tests that show why an outcome changed, not just that it changed
  • cover ambiguity, contradiction, and decision-loop pressure cases
  • treat report artifacts as quality surfaces, not optional extras

First Proof Check

  • packages/bijux-proteomics-intelligence/tests
  • src/bijux_proteomics_intelligence/policies.py and evaluators.py
  • src/bijux_proteomics_intelligence/report/ and outcomes.py

Design Pressure

The easy failure is a test suite that proves numeric movement while leaving the explanation path too thin for a reviewer to trust.