Your dashboard says 100% coverage. Your CI is green. Your tests all pass. And your code is still wrong.

That gap is getting a lot wider. AI can now generate your implementation and your test suite in the same breath. The tests verify what the code does, not what it was supposed to do. You didn't gain a safety net. You gained a very convincing illusion of one.

In this session, we'll pull that illusion apart and replace it with two techniques that actually measure whether your tests do anything:

- Mutation testing, which stops asking "how much of the code do we cover?" and starts asking the only question that matters: "if this code breaks, does a test actually fail?" We'll mutate real code and watch AI-generated test suites let bugs sail straight through.
- Property-based testing, which throws out example-based assertions entirely in favor of invariants that must hold no matter the input. Properties an AI can't accidentally reverse-engineer from the implementation it just wrote, because it never saw the implementation to begin with.

You'll leave with a concrete answer to the question your coverage report can't: where does "correct" actually come from? And a practical toolkit for keeping that answer honest as more of your codebase is written by something other than you.