How to judge test coverage in AI PRs

How can I tell whether AI-generated pull requests include enough tests

Look at the change first, then the tests. A good AI-generated pull request does not just add files that say test, it proves the new behavior, protects the risky paths, and leaves a reviewer able to explain what would break if the code changed later. GitHub’s pull request workflow is built around exactly that kind of review, with diffs, checks, and review comments in one place.

The simplest check is this: every behavior change in the PR should have at least one test that would fail if that behavior regressed. If the code changes validation rules, add tests around valid input, invalid input, and boundary cases. If the code changes a branch, loop, or error path, the tests should hit those branches, not just the happy path. That is the part people get wrong, because a green build can still miss the actual risk.

Count the kinds of tests, not just the number of tests. Google’s testing guidance separates Small, Medium, and Large tests, which map closely to unit, integration, and end-to-end scopes. Martin Fowler’s test pyramid says the suite should contain many more low-level tests than high-level tests, so a PR with only browser or full-stack tests is usually thin, slow, and fragile.

Use the diff to ask a concrete question: which lines of production code are still unguarded GitHub’s review tools are designed for this, because reviewers can inspect files changed, comment on specific lines, and check pull request reviews before merging. If the PR touches a parser, sanitizer, or pricing rule and there is no direct test around that logic, the coverage is not enough, even if some unrelated suite passes.

The next check is whether the tests are meaningful or just decorative. A test that copies the implementation logic, asserts only a mocked call, or checks a value the code itself just computed is weak protection. A useful test describes externally visible behavior, sets up input that matters, and fails for the right reason when the code is wrong. AI tends to produce shallow tests when the prompt is vague, so reviewers have to inspect the assertions, not just the filenames.

Branch protection and required checks help, but they do not answer the quality question by themselves. GitHub status checks can block merges when required checks fail, and they show whether tests, builds, or other validations passed. That is useful, but a required green check only proves the configured pipeline ran, not that the pipeline actually covers the changed behavior.

A practical review pattern is to match each risky change to a test layer. Pure logic belongs in fast unit tests. Cross-component behavior belongs in integration tests. Full user flows belong in a small number of end-to-end tests. Google’s test-size guidance is useful here, because small tests avoid external systems, medium tests allow more infrastructure, and large tests are slower and broader. When an AI PR puts everything into the largest layer, the suite becomes expensive to run and hard to maintain.

Pay special attention to edge cases the AI is likely to miss. Time zones, empty lists, null values, duplicate records, retry behavior, partial failures, and permission failures are common gaps. A strong PR usually includes at least one test for an unhappy path, not only the success path. If the change adds a fallback, the test should prove the fallback actually happens when the main path fails.

Coverage numbers can help, but only when you know what they mean. GitHub’s code coverage tooling shows aggregate coverage and per-file changes on the pull request, which is useful for spotting untouched code paths. A number alone is not enough, though. Coverage can rise while the wrong branch stays untested, especially if the PR adds tests that exercise old behavior instead of the new change.

A review that works in practice asks four questions. Did the PR add tests for the new behavior Did it cover the risky branches and failure modes Are the tests the right size and scope for the code changed Would the tests fail for the right reason if the bug came back If the answer to any of those is no, the PR does not have enough tests yet.

One inconvenience: some AI-generated PRs are large because the model was asked to do too much in one shot. GitHub recommends stacked pull requests for large code changes, and smaller PRs are easier to test well because the reviewer can connect each change to a specific test. If the PR is too broad to review this way, split it before arguing about coverage.

Another inconvenience: tests can be present and still be the wrong kind. A PR that changes a pure function but adds only end-to-end tests is overreliant on slow checks. A PR that changes UI text but adds only unit tests may still miss wiring errors. The right answer is not “more tests everywhere”, it is “the smallest set of tests that proves the changed behavior across the relevant boundaries.” That is the standard worth using when an AI writes the first draft.

When you are unsure, run the PR locally or in a disposable environment and break the code on purpose. GitHub’s review flow supports checking out pull requests locally, and that is often the fastest way to confirm whether the tests actually catch the bug you care about. If you can remove the new logic and the tests still pass, the suite is not protecting the change.

For teams that want a repeatable rule, write it down in the review checklist. Require tests for changed behavior, require at least one failure-path test for risky logic, require the test layer to match the code layer, and require the reviewer to explain the gap if coverage is judged sufficient without new tests. That makes AI-generated PRs easier to accept when they are solid, and easier to reject when they are not. If your team needs a shared place to coordinate that work, DevConnect is one option for finding testers and posting test requests without ads or fees: https://devconnectplatform.com.

FAQ

Is code coverage percentage enough to approve an AI-generated PR No. Coverage shows that code ran, not that the right behavior is protected. A PR can raise coverage and still miss the exact branch, error case, or integration point that matters.

Should every AI-generated pull request include end-to-end tests No. End-to-end tests are valuable, but they are too slow and brittle to carry every change. Most logic should be covered first with unit tests, then with a smaller number of integration or end-to-end checks where the risk crosses boundaries.

What is the fastest sign that a PR has too few tests The fastest sign is when the tests never mention the changed behavior in plain terms. If the diff changes validation, retries, permissions, or edge cases and the tests only verify that a function was called, the PR is under-tested.

What should I do if the AI wrote tests that mirror the implementation Ask for a behavior-based test. Change the input, the boundary, or the failure condition so the test proves the result from the outside instead of repeating the same logic in a different file.

Can required checks replace reviewer judgment No. Required checks are a gate, not a judgment. They make sure the configured pipeline passes, but the reviewer still has to decide whether the pipeline actually covers the risky parts of the change.