Where AI helps in testing, and where it fails

AI changed how we write tests, but it did not change what a good test is. A test earns its keep only when it would fail if the code were wrong. Keep that one rule in mind and the rest of this course falls into place.

Where AI genuinely helps:

• Scaffolding. Boilerplate, fixtures, setup and teardown, and parameterized cases are fast and mostly mechanical.
• Edge cases you forgot. Ask for boundary values, empty inputs, and error paths and the model will list cases you skipped.
• Test data. Realistic sample records and varied inputs are quick to generate.
• Coverage gaps. Point AI at an untested branch and it will draft a case to exercise it.

Where AI tends to fail:

• Assertions. Models are strong at structure and weak at judging what the correct result actually is.
• Integration paths. Anything that needs real domain context or knowledge of how systems connect is shaky.
• False confidence. A generated suite can be all green and still verify nothing.

The pattern for the whole course: let AI draft the parts that are mechanical, and keep the judgment about what is correct firmly with a human. AI proposes; you decide whether the test checks the right behavior.