"Automated tests are the cornerstone of modern software development. They ensure that every time we build new functionalities, we do not break existing features our users rely on. Traditionally, we tackle this with example-based tests. We list specific scenarios (or test cases) that verify the expected behaviour. In a banking application, we might write a test to assert that transferring $100 to a friend's bank account changes their balance from $180 to $280."
"However, example-based tests have a critical flaw. The quality of our software depends on the examples in our test suites. This leaves out a class of scenarios that the authors of the test did not envision - the "unknown unknowns". Generative testing is a more robust method of testing software. It shifts our focus from enumerating examples to verifying the fundamental invariant properties of our system."
Automated tests traditionally use example-based test cases that enumerate specific scenarios to verify expected behavior. Example-based testing depends on the completeness of listed examples and therefore misses unanticipated scenarios, creating "unknown unknowns". Generative testing programmatically generates inputs to exercise a wider range of behaviors and focuses on verifying system invariants rather than preselected examples. Generative tests increase coverage and improve detection of unforeseen bugs. When failures occur, generative frameworks shrink failing inputs to minimal reproducing cases, aiding root-cause analysis and bug fixing. Generative testing encourages developers to specify fundamental properties of systems and yields more robust quality assessment.
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