Insights · AI-assisted QA

AI in Software Testing: What Really Works Today

Generative AI is changing testing faster than any tool generation before it. A sober look at the use cases, the limits – and the compliance questions you should settle before you start.

Use cases with demonstrable value

Test case design: From requirements and acceptance criteria, AI produces draft test cases in minutes, complete with boundary values and negative cases – humans curate instead of typing.
Requirements analysis: AI finds ambiguities, gaps and contradictions in specifications – you can try the underlying criteria yourself with our free tool RequiCheck.
Analysis & reporting: Identify defect clusters, merge duplicates, pre-draft test reports.
Test data: synthetic test data with no GDPR exposure, in any quantity and variance.
Maintaining automated tests: Selector repairs and refactoring suggestions reduce the upkeep of flaky suites.

The limits – and why humans remain essential

AI results are plausible, but not guaranteed to be correct: when in doubt, models invent convincing-sounding test cases for requirements that do not exist. Without expert review, such artifacts flow unfiltered into the test basis. On top of that, responsibility cannot be delegated – a human must be able to stand behind a release recommendation, in front of auditors, clients and regulators.

Our principle: AI takes on the legwork; senior experts retain assessment, decisions and responsibility. It is precisely this interplay that makes AI use audit-ready.

Compliance: EU AI Act & GDPR

GDPR: No real personal data in prompts; get data processing agreements and technical measures (Art. 32) cleanly in place; prefer synthetic test data.
EU AI Act: Document intended uses, ensure human oversight, and meet transparency and governance obligations according to risk class.
Traceability: Version prompts, models and review steps – otherwise no result is reproducible.

How to start sensibly

Choose a clearly scoped pilot area (one team, one product) and collect baseline metrics.
Governance first: Which data may go into which model? Who reviews AI results, and starting when?
Measure for 10 weeks instead of guessing: A structured pilot like the example engagement delivers reliable before-and-after figures.
Upskill the team: The ISTQB® CT-GenAI seminar teaches the tools of the trade for AI-assisted testing.

Next step

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