How it works
AI generated tests are test cases, input data or checks produced with help from a model or AI tool. The tool can use existing code, requirements, comments or examples to suggest scenarios that exercise a function and compare the result with what is expected.
The result is a test proposal, not proof of quality. If the request is vague, AI can pick an interpretation that does not match the product rules. It can also generate tests that only repeat current behavior, including a bug, or that check internal details instead of the outcome the user expects.
A useful review asks: what rule does this test protect? Does the input represent a real situation? Does the expected output come from a reliable requirement? Does the test catch a failure that would matter? These questions help differentiate execution coverage from behavior coverage.
AI can support different verification levels, such as unit tests for functions, integration tests between components and interface tests. The team chooses the level based on the goal and keeps tests inside the normal execution flow, so future changes can be verified.
Practical example
Consider a function that calculates the final value of an order. The person provides the tool with the function signature and discount rules and asks for case suggestions. AI can propose a purchase with no discount, another with a valid discount and an input with an invalid code.
Before accepting the tests, check if the rules are correct and if relevant scenarios are missing, such as empty order, negative value or rounding. If a test expects a result based on an interpretation invented by the tool, it can lock a wrong rule into the project.
Then run the tests and observe if they really fail when the implementation is changed incorrectly. A test that always passes, or that only confirms a function was called, may not protect important behavior. When AI changes code and creates tests at the same time, review both independently to avoid the test just approving its own implementation.
What it's for
Assisted generation can speed up the creation of initial scenarios, suggest edge cases and help understand parts of a code base. It can also turn requirements written in natural language into ideas for checks that the team then reviews and formalizes.
It is most useful when there are clear acceptance criteria, representative examples and a person able to validate the expected results. Without these elements, the model can fill gaps with plausible assumptions. This is especially risky in areas with business rules, security, payments or personal data.
Integrate approved tests into the development flow, keep them readable and update them when requirements change. Record why each case exists and avoid fragile tests that depend on irrelevant details. A well maintained suite combines automation and human judgment.
In summary, AI helps produce test candidates; the team decides which cases represent correct behavior and confirms they detect relevant regressions. The benefit comes from continuous review and execution, not just the number of tests generated.