Why using AI to test software is here to stay?

Why AI-driven testing matters

While writing enterprise code, software purists usually recommend writing unit tests manually. The intent is noble: unit tests are expected to check the functionality of each class, and AI would not know the expected behavior because it only sees the implementation, not the “why” behind the code. That’s the motive behind TDD (Test Driven Development): write the tests first, then fill in the code. After each commit, running the tests tells the developer whether the code is functionally correct. All of this worked well pre-AI.

With the advent of AI—especially high-reasoning LLMs—this has changed. Writing tests is a labor of discipline and painstaking effort. Developers often skip writing unit tests or do a shoddy job of it. Can AI help? Absolutely.

The case against TDD (Test Driven Development)

With AI, TDD has taken a backseat. AI-generated unit tests have become more reactive than proactive, with the tests written after the code is written. Purists would say this is wrong, but it works better than expected.

It is similar to how snapshot testing in frameworks like Jest works. Snapshot testing is reactive: at a point in time both the tests and the code are expected to be correct, and the snapshot tests run after a code change. When there’s a failing test, you either fix the test or fix the code. The developer still reviews the code and ensures it “looks” right— this can never be skipped. For the failing test, AI steps in and conforms the test to the code, not the other way around.

TDD still holds value; its role has been transformed. AI is speeding up the unit testing process by handling the reactive fixes while developers keep their focus on code quality.