Section 2 Review
You've finished AI-Assisted Testing Techniques, the second section of AI for QA. This page is a dedicated recap — bookmark it as a fast reference, separate from the modules themselves.
Section Summary
✔ AI-Assisted Test Case Generation — using AI to draft test cases, then applying Boundary Value Analysis and Equivalence Partitioning explicitly to find what the draft's obvious-shape coverage misses ✔ AI-Assisted Test Data Creation — evaluating AI-generated test data against existing volume/shape/distribution criteria, and checking structured values against real validation logic, not just surface format ✔ AI-Assisted API and Automation Authoring — reviewing AI-generated test code against existing API accuracy and automation-quality standards (Page Object Model, explicit waits, precise assertions) ✔ AI-Assisted Defect Analysis and Exploratory Testing — treating an AI-suggested root cause as a hypothesis requiring verification, and AI-generated exploratory charters as a starting point, not a substitute for human discovery
How they build on each other: each module in this section applies Section 1's foundational judgment and hallucination-recognition skill to a specific, real QA productivity task — none introduce new review principles, all apply the same core discipline (verify against a real, specific target) to a different kind of AI-generated artifact.
Section 2 Quick Reference
| AI-Generated Artifact | What to Check It Against |
|---|---|
| Test cases | Boundary Value Analysis / Equivalence Partitioning coverage; expected results against the real requirement |
| Test data | Volume, shape, distribution; structured values against real validation logic |
| API/automation code | Real endpoint accuracy; Page Object Model, explicit waits, precise assertions |
| Suggested root cause | Direct verification against real logs/code/data before trusting |
| Exploratory charters | Useful starting point; genuine discovery still requires human exploration |
Section 2 Knowledge Check
Five realistic scenarios. For each, decide which module's framework applies. No answers are provided here. Solutions: Section 2 Solutions.
Scenario 1: An AI-drafted test case set for a "minimum order value of $25" rule includes cases for $10 and $50, but nothing else.
Scenario 2: An AI-generated batch of 300 sample customer records all have suspiciously similar account-creation dates.
Scenario 3: An AI-generated automation script for a checkout flow includes sleep(5) before checking the confirmation page.
Scenario 4: An AI tool confidently explains that a failing test is caused by "a timezone conversion bug," based on a stack trace.
Scenario 5: A tester uses an AI-generated list of exploratory charters and considers the exploratory testing session complete once every charter on the list has been tried.
Continue to Section 3
Testing AI-Driven Features, starting with Testing LLM-Generated Content — where this path shifts from using AI to accelerate testing, to testing AI itself as a product feature.