Use AI to strengthen software testing, not just write more tests

A large test suite can still miss the behaviour customers depend on. AI-generated tests are most useful when they begin with risks and requirements rather than simply repeating the code being tested.

A workable implementation

List important user journeys, failure conditions, and boundary cases. Ask the model to suggest missing cases, then have an engineer select meaningful tests. Run them against both correct behaviour and deliberately broken examples to assess whether they detect errors.

Where human review matters

Keep test data synthetic or properly approved. Inspect assertions for false confidence: a test that merely checks whether a page loads does not verify that a payment amount or permission rule is correct.

What to measure

Track important defects caught before release, flaky-test rate, and time spent maintaining the suite. Review failures for relevance before adding another automated check.

How BPV can apply this

BPV can include AI-assisted test design in a software project while keeping engineers responsible for expected outcomes. Start with one business-critical journey and build evidence before expanding.

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