Software Testing, Code Review, and AI-Generated Code Verification

The diff looks right. The tests are green. The assistant wrote both, and both pass. Do you ship it? Most quality practice was built for a world where a human wrote the code and could explain it. That world is fading. Coding assistants now produce plausible logic that quietly drops an edge case, import packages that don't exist, reach for an insecure default, and — most seductively — write tests that assert whatever the code already does, right or wrong. The green checkmark has never been easier to earn or less worth trusting. This course rebuilds verification as a discipline you can actually defend. You'll learn the test-design techniques the field has relied on for decades — equivalence partitioning, boundary analysis, decision tables, state and combinatorial models — so your tests find defects instead of chasing a coverage number. You'll learn to review code the way the strongest engineering orgs do: a systematic pass over design, correctness, security, and maintainability, not a rubber stamp or a style fight. You'll learn to read coverage honestly and use mutation testing to expose suites that pass without testing anything. And you'll build quality gates in CI — coverage, mutation, static analysis, secret and dependency scanning — that turn your standards into something a change has to clear before it reaches production. Running through all of it is the part no older course can give you: treating AI-generated code as untrusted input with a known defect signature, and hunting it deliberately. Forty-one lessons, each one capability you can apply the same day. By the end, given any change — a teammate's or an assistant's — you'll know exactly what to test, what to review, and what to let through.