7.9Hands-On Exercise
- Take a user story from your current sprint and use an AI tool (ChatGPT, Claude, or a specialized tool) to generate test cases. How many edge cases did it find that you would have missed?
- Review 5 AI-generated test cases critically. Which are good? Which need modification? Which are wrong?
- Use AI to generate test data for one of your features. Evaluate the quality and coverage.
- If your team has flaky tests, analyze the failure patterns manually. Could an AI tool have detected them faster?
- Create a prompt template for test case generation that includes your project's domain context and standards.
Interview Talking Point: "I use test management tools to maintain traceability from requirements to test cases to defects, so we can always answer the question 'what was tested for this release and what are the known gaps?' I write JQL queries to surface defect trends and build dashboards that update automatically from CI/CD pipeline results. I tailor quality reports to the audience -- sprint review summaries for stakeholders, trend analysis for engineering leads, and operational metrics for QA retrospectives. I have also started using AI to draft test cases from user stories -- it catches edge cases like security inputs and rate limiting that are easy to miss under sprint pressure, and then I review and refine the output. The AI amplifies my coverage without replacing my judgment."