A test’s expected result is the reference used to decide whether observed behavior is acceptable. In an AI-generated test, that reference can be generated from the same mistaken assumption as the implementation. Passing then establishes agreement between two artifacts without independently establishing correctness.
The review must therefore inspect the assertion’s origin. A test is useful when it can detect a meaningful deviation from an externally defined requirement, not simply when it runs successfully against the code that inspired it.
Establish an independent example
Select important business behavior and obtain expected results from an independently checked calculation, an accepted specification, or reviewed examples supplied by the responsible domain owner.
Suppose generated code rounds an amount at the wrong stage and its generated test repeats that calculation to obtain the expected value. Both can agree while violating the intended rule. A separately worked example with a boundary value can expose the shared mistake.
Independence concerns reasoning and evidence, not just using a second file or another generation request. Rephrasing the same unsupported assumption does not create an independent oracle.
Review assertions and fixtures together
An assertion can look precise while its fixture excludes the condition that matters. A test for an approval rule needs amounts, actors, states, and exceptions that exercise the intended boundary.
Review missing negative cases, invalid inputs, permission failures, and interrupted operations. Also check whether generated changes weaken assertions, skip tests, or delete failing cases instead of resolving the implementation problem.
GitHub’s AI-code review guidance identifies ignored constraints, hallucinated APIs or packages, and deleted or skipped tests as review concerns. These are concrete checks for generated changes, not a measured defect rate for every tool or task.
A baseline needs approval of meaning
Snapshot or approval testing records selected output and compares later runs against an accepted reference. ApprovalTests describes manual checking before accepting that reference.
The reference can preserve a known defect if it is accepted without business review. Characterizing current behavior and specifying correct future behavior should therefore remain distinct decisions.
When a target differs, classify the difference before updating the baseline. A bulk snapshot refresh can erase evidence of a regression just as readily as it can record an intentional redesign.
Execution coverage measures a narrower property
Line coverage indicates which code executed during the tests. It does not establish that the tests would fail if a meaningful result were wrong. An exercised calculation with no relevant assertion can contribute coverage without verifying its output.
PIT mutation testing changes application code and checks whether tests detect the alteration. This supplies evidence about fault detection beyond simple execution coverage within its supported tooling and mutation scope.
Surviving mutations need interpretation. Some can expose weak assertions or missing cases; others can be behaviorally equivalent for the relevant contract. A score does not remove the need to examine what was changed and tested.
Acceptance belongs outside the generated implementation
A useful review can introduce a representative wrong result, removed check, or altered boundary and verify that the suite rejects it. The selected fault should reflect a consequential requirement rather than merely increasing a score.
Keep the expected result’s provenance with the test or its supporting evidence. Later maintainers should be able to explain why the assertion is correct and what coverage it provides.
Generated tests can supply candidate fixtures and assertions efficiently in a particular workflow, but no productivity rate is established here. Their accepted value is demonstrated detection of meaningful incorrect behavior in the required scenarios.