Improving Test Reliability by Clearing Seeded Data
When writing tests for features that rely on seeded or pre-existing data, it's crucial to ensure a clean and consistent state before making assertions. Failing to do so can lead to flaky tests and unreliable results, especially in scenarios involving random data selection.
Consider a test suite designed to verify the behavior of a "random mode" feature in our application.