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An AI code-review finding should become a deterministic guard only after a maintainer verifies the behavior, agrees on the invariant the product should preserve, and chooses a test that can actually observe it. A model finding is a lead, not a confirmed defect; a passing test is evidence only for the cases and layer it exercises.
Which input or state triggers the behavior?
Start by making the finding concrete. Identify the input, state, or sequence of actions that could trigger the reported behavior, then route the question to a maintainer who understands the affected boundary or can find its owner. AI can help locate relevant code, but people remain responsible for deciding whether the behavior matters and what the product should do.
Reduce the claim to a reproducible case where possible: a unit test, integration fixture, browser path, or packaged-runtime scenario. Record the environment and version along with the reproduction. If the behavior does not reproduce, preserve what you tried and why the hypothesis was rejected. “Could not reproduce” is not proof that the behavior cannot happen; the environment or available evidence may be incomplete.
What does the system do?
Describe the observed behavior without treating the model’s interpretation as established fact. Separate the steps you can demonstrate from assumptions about code paths, user impact, or intended behavior. For example, a report might allege that a failed import replaces the project currently selected. Verify whether the import can fail validation and whether the selected project actually changes in that case.
What invariant is violated?
Before writing a test, state the behavior that should remain true in language a reviewer can approve. The illustrative invariant here is: “An import that fails validation must not replace the currently selected project.” That sentence is useful only if the maintainer agrees it reflects intended product behavior; a test makes its assertion durable policy, not merely a record of what the model suggested.
Not every validated observation belongs in a regression test. A design trade-off may call for a decision record; a threat hypothesis may need specialist security review; a style suggestion might be handled by a formatter—or require no action. Other sound outcomes include using a different review or test method, documenting why a finding is rejected, deferring it to an issue while evidence is missing, or escalating it for specialist review.
| Response | When it fits | Main trade-off |
|---|---|---|
| Regression guard | The behavior is reproduced, the invariant is approved, and a test can observe it reliably. | Creates a durable check, but a poorly chosen assertion can be brittle or produce false alarms. |
| Another test or review method | The finding is credible, but the relevant behavior is not observable at the proposed test layer. | Can provide better evidence than a mismatched unit test, though it may cost more to run or maintain. |
| Documented rejection | Evidence shows the reported behavior is not a defect or the interpretation is wrong. | Closes the finding with a reason, but does not establish that untested scenarios are impossible. |
| Deferred issue | Important evidence, access, or reproduction steps are missing. | Keeps uncertainty visible, but does not guard against a regression until resolved. |
| Specialist escalation | The decision needs expertise such as security or domain-specific review. | Adds informed scrutiny, but does not itself create an automated check. |
What would a correct result look like?
Choose the lowest test layer that can reliably detect the behavior being claimed, and keep the assertion focused on the approved invariant. A parser rule may be covered by a unit test; a persistence boundary may require an integration test; a claim about first launch may require running the packaged application. Evidence at one layer does not prove behavior at another.
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- Encode the invariant. Build the smallest durable test that exercises the relevant input or state transition and asserts the approved result.
- Check that the guard detects the failure. Run it against the known failing case. Where appropriate, temporarily remove or invert the behavior to confirm the test fails for the defect it is meant to catch.
- Check representative valid cases. Run supported cases that should continue to work, so the guard does not prevent correct behavior while catching the regression.
- Record the evidence and scope. Keep the fixture identity and expected behavior, command and relevant environment, source revision, execution location (such as local, CI, browser, or packaged artifact), and known blind spots or deferred platform paths with the result.
A green run alone does not show that a test has useful signal. The failure case and representative supported cases help establish what the guard checks; they do not prove unexercised paths are safe.
Which evidence supports the claim?
Keep the conclusion no broader than the evidence. A local pass shows that a particular check passed for a particular revision and environment. A test run in a browser does not establish first-launch behavior in a packaged application, and a unit test does not establish persistence behavior it never exercises. Recording provenance makes it harder for a narrow result to be retold as broad assurance.
This is consistent with the broader purpose of SLSA specification v1.2, which describes a framework for incrementally improving software supply-chain security and includes recommended attestation formats such as provenance. SLSA does not prescribe this article’s test-record checklist; the connection is the value of making evidence traceable.
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Does the guard need to block a merge?
A passing local test and an enforced merge check are different facts. If the behavior must block a merge, connect the check to repository policy and verify that policy applies to the relevant workflow and revision. GitHub’s rulesets documentation describes controls for selected branches and tags: active rulesets are enforced, while disabled rulesets are not. Rulesets can work alongside branch protection, and availability and exact configuration depend on the platform and account context.
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