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1. Establish what the change is supposed to do
Before judging the tests, read the code change alongside its task description, acceptance criteria, relevant documentation, and nearby tests. Identify the public behavior or risks the change introduces, then map each generated test to one of those requirements. GitHub recommends checking AI-generated code against the project’s purpose, architecture, trusted documentation, and conventions: GitHub’s AI-generated code review guidance.
Do not treat a test’s expected value as authoritative simply because the AI supplied it. Confirm expected results against documented requirements and realistic behavior. If a business rule is undocumented, ask the product owner or domain expert rather than accepting a model’s guess; GitHub cautions that Copilot may not infer undocumented business rules: GitHub’s guidance on increasing test coverage.
2. Run the tests in the project’s ordinary workflow
Use the project’s normal test command or CI path, and check more than the final pass/fail status. Verify that the tests were discovered and executed, review warnings and failures, and run the static analysis used by the project. Look for tests that were disabled, skipped, or deleted: those changes can hide a problem instead of fixing it. GitHub recommends automated tests and static analysis as early functional checks and specifically flags skipped or deleted tests as a review concern: GitHub’s AI-generated code review guidance.
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A test that passes only because the runner never found it is not evidence of coverage. If the ordinary workflow does not execute the new tests, resolve discovery or configuration issues before relying on them.
3. Read each test as a claim about behavior
For every test, state in plain language what behavior it claims to protect. Then inspect its setup, inputs, action, and expected outcome. Ask whether the assertion would fail if that behavior regressed.
- Is the expected result supported? Check it against requirements and domain behavior, not just the implementation or the model’s explanation.
- Does the assertion distinguish correct from incorrect behavior? A test can execute the relevant code and still be too weak to catch a plausible defect.
- Is the test checking behavior or incidental detail? Assertions tied unnecessarily to internal structure may break during harmless refactoring.
- Are mocks and fixtures credible? An unrealistic mock can make a test pass while bypassing the interaction or state that matters.
Generated tests may omit scenarios, so review their logic and add cases where needed. GitHub’s test-writing guidance puts it plainly: “The tests that Copilot generates may not cover all scenarios, so you should always review the generated code and add any additional tests that may be necessary.” Source: GitHub Docs, Writing tests with GitHub Copilot.
4. Check branches, boundaries, and failure behavior
List the decisions and conditions in the changed logic. For each important branch, check whether the suite represents the relevant outcomes and asserts what should happen. Happy-path tests alone can miss regressions in boundary conditions or error handling.
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- Normal valid input and representative realistic values
- Boundary values, such as the smallest or largest supported value
- Empty or null input, when the interface permits it
- Invalid states and expected validation errors
- Failure paths, including dependency errors or unsuccessful operations
- Changed state transitions, authorization boundaries, persistence, or external interactions that merit integration-level checks
Do not add cases mechanically: first establish which inputs and failures are valid for the contract. GitHub recommends prompting for edge cases and branches, while warning that happy-path-only coverage can miss regressions: GitHub’s test-writing guidance and GitHub’s coverage guidance.
5. Use coverage reports as a map, not a verdict
Line and branch coverage can help locate changed or important code that no test executes. Microsoft describes code coverage as the proportion of project code run by tests; it measures execution, not whether the tests would detect a behavioral defect: Microsoft’s Visual Studio testing tools overview. A line can execute while its result is never meaningfully asserted.
For a stronger check, use mutation testing where appropriate: introduce a small fault, such as changing a condition or value, and see whether the suite fails. Google describes mutation testing as injecting bugs to evaluate whether tests detect them: Google Testing Blog, Mutation Testing (April 2021). A meaningful mutant that survives is a prompt to inspect missing scenarios or weak assertions; equivalent or irrelevant mutations still require judgment.
There is no universal coverage percentage that proves AI-generated tests are meaningful. Set project thresholds in light of risk and use coverage as a signal alongside behavioral review, not as a substitute for it.
6. Check readability, maintainability, and project fit
Tests should make their intended behavior easy for another developer to understand and should fit local patterns. Review fixtures and mocks for brittle coupling, and check that added dependencies are real, maintained, and acceptably licensed. GitHub’s review guidance identifies readability, dependencies, licenses, and suspicious or hallucinated packages as concerns: GitHub’s AI-generated code review guidance.
When comparing generated tests with existing or human-written tests, assess both against the same criteria:
- Alignment with requirements and observable behavior
- Execution of changed lines and important branches
- Assertion strength and ability to reveal faults
- Realistic normal, edge, and failure scenarios
- Clarity, stability, maintainability, and consistency with project conventions
- Appropriate test level—unit, integration, or end-to-end—and execution in CI
- Cost to run and maintain, where relevant
7. Decide what to accept and record what remains uncovered
Accept a generated test only when its behavior is understood, its expected result is grounded in the contract, its assertions are credible, relevant risks are represented, and it runs reliably in the project workflow. Otherwise, revise the assertion, add missing cases, or reject the test if it encodes an unsupported assumption.
Record uncovered requirements or risks explicitly; do not present a coverage percentage as a complete quality judgment. For teams rolling out AI-assisted testing, GitHub also suggests tracking post-deployment bug reports, developer confidence, and time spent writing tests alongside line and branch coverage. These are suggested monitoring measures, not proof that any particular generated suite is effective: GitHub’s coverage guidance.
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Microsoft’s testing-tools overview says GitHub Copilot testing for .NET is available starting in Visual Studio 2026 Insiders and describes it as generating, debugging, and running tests. The page also notes that some testing and coverage tools have version or edition limitations. Check the current product edition and availability before following setup instructions: Microsoft Learn.
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