Turning test ideas into automated checks
Have a coding model turn test ideas, scenarios or manual test cases into automated tests in the team's own framework, conventions and helpers.
How it works
Writing the automation for an agreed test idea is often slower than deciding the idea. A coding model, given the test idea, the code under test and examples of existing tests in the repository, drafts the new test following the same structure, fixtures, page objects and naming. Pointing it at the project's helpers matters; without them it reinvents setup code the team already has. The draft is run, and the model iterates on failures until the test passes for the right reason. The important review question is not whether it passes but what it asserts — whether the check would actually fail if the behaviour broke.
When to use it
Backlogs of agreed test cases waiting for automation; migrating manual regression suites; adding tests for bug fixes; and teams with a well-structured existing suite the model can imitate.
Limitations
Generated tests tend to assert too little (the page loaded, the call returned 200) or to assert whatever the current behaviour is, bugs included. A model asked to make a failing test pass may change the assertion instead of reporting a bug. Volume becomes a liability: many shallow tests are slow, flaky and costly to maintain. Code quality varies with how consistent the existing suite is.
Watch for
A regression test for a bug fix that passes with or without the fix, because the model asserted on a value its own setup put there. Run each new test once against the unfixed code, or with the behaviour broken, and confirm it fails.
What you still own
Reviewing what each test asserts, deciding which tests are worth keeping, and treating a test the model had to bend to pass as a possible bug.
Cite this
Qlarify Labs. (2026). Turning test ideas into automated checks. Retrieved from https://labs.qlarify.fi/ai-in-testing/turning-test-ideas-into-automated-checks