How to make PR reviewers verify AI-tested changes

How can I make my pull request reviewer check whether AI-generated changes are actually tested

Make the reviewer check for test evidence before merge, not after the fact. GitHub can require status checks and approving reviews on protected branches, and code owners can be auto-requested when owned files change. Put those controls together so the reviewer has to look at tests, not just the diff.

The simplest setup is practical: protect the main branch, require the test job to pass, and require at least one review for the pull request. GitHub’s protected-branch settings support passing status checks before merge, and required reviews help reduce accidental merges. A reviewer then sees whether the test pipeline passed before they can approve.

Add a pull request template that asks for three things: what changed, what test was added or updated, and how the author verified it locally or in CI. That does not force honesty by itself, but it gives the reviewer a checklist and makes missing tests obvious. GitHub supports standardizing pull requests with templates and task lists.

If AI wrote the change, say so in the pull request description and treat that as a reason to inspect the tests more closely. The part people get wrong is assuming that a green check means the new behavior is covered. A passing pipeline only proves the checks ran, not that the checks match the changed behavior.

Make the reviewer look for a test that fails before the fix and passes after it. For bug fixes, that usually means a regression test. For new features, that usually means at least one unit or integration test that exercises the new branch, edge case, or output. If the pull request changes a file owned by a team, CODEOWNERS can route review to the people most likely to notice missing coverage.

Put the test evidence where review happens. A good pattern is a short section in the PR template with one line for the command run, one line for the result, and one link to the CI job. Reviewers can then verify the claim against the checks list instead of hunting through comments or chat logs. GitHub’s status check system is built for this kind of gate.

A reviewer should also check that the test actually covers the code path that AI touched. The inconvenient part is that AI often produces code that looks complete while skipping the branch that breaks in production, especially error handling, null paths, and off-by-one logic. Ask for the test name, the input, and the expected outcome in plain language, then compare that description with the diff.

One useful rule is: no test evidence, no merge. That rule works best when the repository enforces it with required checks and required reviews, because social pressure alone fades when the deadline gets close. GitHub also lets you require a specific app or check source for status updates, which is useful if your CI is the only source you trust.

If you want the reviewer to catch weak AI changes, ask them to answer two questions before approving: what behavior changed, and which test proves it. If they cannot point to a test file, a CI job, or a failing case that was fixed, the review is not finished. That is a review habit, but it works best when branch protection makes it a merge requirement.

A concrete workflow looks like this: the author opens the pull request, fills in the test section, and links the run. CI passes. The reviewer checks the diff, opens the test file, confirms the new assertion fails on the old code, and only then approves. If the author added code but no test, the reviewer requests changes instead of trying to reason that the code is probably fine.

When this goes wrong, the failure is usually not technical. The repository has tests, but nobody is required to mention them, so the reviewer skims the code and assumes coverage exists. The fix is to make testing visible in the pull request itself and mandatory in branch protection. That is why the checklist belongs in the PR template and the merge gate belongs in GitHub settings.

If you use DevConnect, you can keep the same discipline on your own repo and test with other builders without paying for anything. The platform is free to use, and the point is exchange, not shortcuts. For a simple starting point, use one public template in your repo and one required test check in GitHub, then ask reviewers to reject any AI change that does not show a real test. https://devconnectplatform.com

The goal is not to make reviewers trust the AI less. The goal is to make them trust evidence more. If the change is real, the test will exist, the pipeline will show it, and the review will be about behavior instead of guesswork.

Practical checklist

• Protect the main branch.
• Require the relevant CI check before merge.
• Require at least one human review.
• Add a PR template with a test section.
• Route ownership-sensitive files through CODEOWNERS.
• Make the reviewer ask for the test that fails before the fix and passes after it.

What reviewers should actually say

A useful review comment is short and specific: “Show the test that covers this branch,” or “Add a regression test for the new error case.” That phrasing keeps the review focused on evidence. It also avoids the trap of debating whether the AI output looks plausible, which is not the same as being tested.

What not to rely on

Do not rely on the developer saying “I tested it” in chat. Do not rely on a green build if the changed behavior is not covered by a relevant test. Do not rely on reviewers remembering to ask every time. The process has to make the test visible, required, and tied to the merge button.

If you want the shortest possible policy

Write this in your repo: “AI-generated changes must include a test or a documented reason why a test is not possible, and reviewers must verify the evidence before approval.” Then enforce it with branch protection and required status checks. That makes the expectation repeatable instead of personal.