AI-generated tests merely repeat the implementation: how can examples that expose real errors be added?
When tests repeat implementation logic, both may share the same bug. State observable requirements and derive expectations from manually explainable samples or independent evidence, then add relevant boundaries and properties.
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When tests repeat implementation logic, both may share the same bug. State observable requirements and derive expectations from manually explainable samples or independent evidence, then add relevant boundaries and properties. Showing rejection of a specific incorrect variant demonstrates more than merely adding assertion count.
Identify assertions that duplicate the same calculation
This applies to tests repeating a production formula or asserting only nonempty output without important contents. Select a key business rule with inputs, expected results and reasoning. Coverage shows executed paths, not correct expectations. Snapshots record present output but do not automatically establish that it meets requirements.
Derive independent expectations from observable requirements
Check whether assertions call the tested function to compute their own expected results or reuse the same production helper. Distinguish a truly independent reference implementation from renamed duplication. Overly simple examples can give identical outputs for several wrong algorithms; find a boundary that separates correct and incorrect behavior.
Add relevant boundaries, properties and targeted faulty variants
Add manually checkable cases, relevant boundaries and valid invariants, such as input reordering preserving a set-based statistic. Target faulty variants and confirm an omitted condition or changed comparison causes failure. Properties and mutations must reflect requirements rather than force assertions about internal details solely to kill irrelevant mutants.
Show the tests rejecting a concrete incorrect implementation
Show a known wrong implementation rejected and the correct implementation passing the same rule. Failure messages should identify the sample and violated requirement. For tasks with multiple valid outputs, check validity rather than one fixed text. Re-recorded snapshots or expectations changed to match current output require renewed requirements review.
This article ran no mutation testing and reports no coverage or detection percentages. Independent expectations and task understanding determine quality; tool reports cannot replace them. Generated property tests also need reproducible failing inputs. Reversible low-impact changes do not need large formal test suites unrelated to risk; prioritize checks that detect meaningful errors.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.