Quick answer
AI test generation tools read your code and automatically write unit tests for it, which is genuinely fast — but the tests they write are only as good as the tool's understanding of what your code is supposed to do, not what it happens to do.
Writing tests is one of those tasks every developer agrees is important and most developers under-do anyway, because it's repetitive and doesn't feel like building. Tools like Qodo aim straight at that gap: point them at a function or a whole file, and they generate a set of unit tests for it in seconds.
How it actually generates a test
The AI reads the function's code, its inputs and outputs, and any surrounding context — related files, existing test patterns in your repo — and infers what behavior is worth checking: typical inputs, edge cases like empty arrays or null values, and error conditions. It then writes test code in whatever framework your project already uses, following the conventions it can detect from your existing tests.
What it's genuinely good at
- Covering the boring-but-necessary cases fast — empty inputs, boundary values, obvious error paths — that developers often skip under deadline pressure.
- Generating a baseline test suite for legacy code that has no tests at all, which is a much better starting point than nothing.
- Keeping tests in sync with a refactor by regenerating them alongside code changes rather than leaving them stale.
- Flagging when a change breaks existing behavior the tests previously encoded, before it reaches review.
Where it runs into a real limit
A test is only useful if it checks that the code does what it's supposed to do — and "supposed to do" is a business requirement, not something visible from reading the code alone. If your function has a bug, an AI reading only that function will often write a test that confirms the bug, because from the code's perspective, the buggy behavior looks intentional. The AI can't tell "this is what the code does" apart from "this is what the code should do" without more context than the function itself provides.
So does it actually catch bugs?
It catches regressions — once a test exists, it will reliably flag future changes that break that specific behavior. That's valuable. What it's much weaker at is catching bugs that already existed before the test was written, because generating a test from buggy code tends to encode the bug as expected behavior rather than surface it.
The practical workflow that works: write the specification or expected behavior yourself (even briefly, in a comment or prompt), let the AI generate the test cases against that spec, then review the generated assertions rather than trusting them blindly. Used that way it's a speed multiplier. Used as "generate tests and assume coverage means correctness," it gives you false confidence with good-looking coverage numbers.
High test coverage generated by AI can be a trap: the metric goes up, but if the tests were inferred from the code rather than from a spec, they may just be confirming bugs are working as coded.
Related reading
Bottom line
AI test generation is excellent at speed and coverage of the obvious cases, and genuinely useful for catching future regressions. It's not a substitute for someone deciding what "correct" actually means before the tests get written.
