The deliberate management of the data used in automated tests, including how it is created, seeded, isolated per test, and cleaned up after execution. Good test data control ensures that each test runs against a known, predictable state and does not depend on or pollute shared data. Approaches include explicit fixtures (pre-defined data sets loaded before a test), seeded data (data inserted directly into a database or system before the test runs), and cleanup passes (removing test-generated data after execution). AI-generated automation code often lacks proper test data control, making it one of the key areas a tester must review and strengthen before committing scripts to a CI/CD pipeline.
Test data control
26th May 2026
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