AI-assisted test generation uses prompts, specifications or code context to suggest test cases or test code. The suggestions can help engineers explore coverage, but they require review, execution and maintenance like any other test artifact.
Why this matters
Generated tests may repeat existing coverage, misunderstand a requirement or encode the wrong expected behavior. A controlled workflow makes the source and review decisions visible.
A practical approach
- Provide a specific requirement or behavior and the relevant approved context.
A practical example
Ask for test ideas or scaffolding rather than unreviewed test authority.
What to keep in mind
Check each proposed case against acceptance criteria, boundary conditions and safety assumptions.
Frequently asked questions
Run the tests and inspect whether they fail when the behavior is intentionally incorrect.
Track generated test ownership and maintenance in the normal engineering process.
Further reading
The views in this article are my own. Product capabilities and availability can change; consult current vendor documentation and your organization’s requirements.
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