AI for Testing FAQ
Use AI to save time, not to remove accountability. This FAQ focuses on safe starting points, review expectations, and the habits that keep AI-assisted testing credible.
Use it to draft test ideas, summarize defects, or turn notes into cleaner documentation. These are useful starting points because a tester can still review the full output before anything important depends on it.
No. Treat them as drafts. Review the logic, run the checks, add missing edge cases, and make sure the scenarios reflect actual product risk rather than generic coverage.
Yes. It can help sketch selectors, assertions, helpers, or refactors. The danger is not using it. The danger is merging brittle or incorrect code without review, execution, and cleanup.
Use the least sensitive information possible, remove secrets and personal data, and follow your organization’s policy before pasting product details into any external tool.
Save prompts when they matter, keep important outputs in version control, and document what was accepted, edited, or rejected so future reviewers can understand the path to the final result.
It tends to help most around analysis and drafting: requirement decomposition, scenario brainstorming, log summarization, and first-pass code or documentation.
It creates risk when teams skip review, accept made-up details, leak data, or treat generated output as evidence that a product is correct.
Yes. Even a lightweight policy helps by covering approved tools, restricted data, review expectations, and which artifacts need to be traceable.
Compare time saved, quality of drafts, review cost, and failure rate. If the tool creates more cleanup than value, it is not helping even if it looks impressive in a demo.
CT-GenAI should be framed as relevant for using generative AI responsibly and effectively in testing.