Review AI-Generated Test Cases Against Product Risk

What you will learn

AI can suggest test ideas and can assume behavior that was never promised. Start with the acceptance criteria and risk, then check the suggestions against documented behavior. Equivalence partitioning and boundary value analysis can help derive a compact set of cases; neither authorizes guessing how an undocumented input should behave.

Worked example

The reviewed example ties assertions to a requirement, input or explicit risk.

Try it

Use the fictional 1–40 character contract to ask an assistant for positive, negative and boundary cases. Group inputs by expected behavior, test the values at each boundary, and label every expected result as contract-supported or still a product question. Add one important case the assistant missed.

Review checklist

• Write the requirement and risk before prompting.
• Group inputs by documented behavior using equivalence partitions.
• Check values at and just beyond documented boundaries.
• Include missing, null, empty and no-result cases only with contract-backed expectations.
• Separate known behavior from open product questions.
• Use synthetic values and approved data handling.
• Record why a suggestion was changed or rejected.
• Keep a human reviewer accountable for test intent.

• ISTQB Certified Tester Foundation Level Syllabus v4.0.1