Human review is the control point
AI-generated test cases should be reviewed with the same professional judgment applied to manually designed tests—plus additional checks for hallucination, duplication, and unsupported assumptions.
Eight review dimensions
| Review area | Question |
|---|---|
| Requirement alignment | Does the case actually validate the supplied requirement or risk? |
| Business correctness | Did AI invent a rule, state, role, or outcome? |
| Coverage | Are important positive, negative, boundary, and integration scenarios represented? |
| Duplication | Is this materially different from another case? |
| Expected result | Is the result specific, observable, and supported? |
| Test data | Are assumptions and data conditions valid? |
| Priority | Is the case important enough to retain? |
| Automation suitability | Is it repeatable, stable, and valuable enough to automate? |
Reject hallucinated business logic
If the requirement never defines that threshold or permission, the test is not evidence—it is an invented business rule.
Consolidate duplicates
AI may express the same scenario several ways. Keep the version that best represents the risk and remove superficial variations that add maintenance without new coverage.
Check the expected result carefully
Expected results are a common hallucination point. Confirm that each expected outcome is supported by requirements, business rules, system behaviour, or an authoritative source.
Review coverage as a set
Do not review each case only in isolation. Review the entire generated set for coverage balance:
- Happy path
- Negative path
- Boundaries
- Permissions
- Data variation
- Dependencies
- Error handling
- Recovery/operability
Track review outcomes
Classify generated cases as retained, modified, rejected, duplicate, or deferred. These decisions create useful data for improving prompts and measuring AI quality.