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How to Test and Validate AI-Generated Changes Before Opening a Pull Request

Validate AI-generated changes with your repository’s own tests, careful diff review, and an honest report of every check that passed, failed, or could not run.

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Before opening a pull request with AI-generated code, verify the change against the repository’s own requirements and test conventions: inspect the diff, run focused tests, then run the related suite. Review the output and code yourself, and report exactly what ran, passed, failed, or could not be run. There is no universal test command, and a green test run is useful only if the tests actually cover the intended behavior.

1. Learn the repository’s test conventions

Start by checking how this project validates changes before asking an AI coding agent to add or run tests. Find the test framework, where tests belong, and the commands used for a single test file and the related suite. Look for an existing test that demonstrates the project’s naming, assertions, and mocking style. Follow those conventions rather than introducing a second test runner. VS Code’s testing guidance recommends grounding test work in the existing project.

There is no one command that applies to every repository. Use the commands documented by the project, such as in its contribution guide or package scripts, rather than assuming a particular language or tool.

2. Define what the change should do

Before judging the implementation, describe the behavior that needs to change and the result a user or another part of the system should observe. Use that requirement to decide what the tests must establish. Include relevant boundary conditions and error cases, not only the normal path.

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Check that a test’s expected result is independent of the implementation. If the test calculates its expected value by calling the same function it is testing, a defect in that function may appear in both places and the test can still pass.

3. Run the smallest relevant tests first

Begin with the narrowest test selection that covers the changed behavior, such as one test file or a focused group of cases. Focused runs usually make feedback easier to interpret: when a test fails, there are fewer unrelated changes to investigate. Record the exact command and its actual outcome, including passed, failed, and skipped tests.

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A command that could not run because a dependency, service, or environment setting was unavailable has not verified the change. Mark that check as unverified rather than treating it as a pass.

4. Diagnose failures without weakening the test

When a test fails, work out whether the cause is a setup problem, an incorrect expectation, or a defect in the implementation. Fix test setup when it is wrong. Compare an expectation with the agreed behavior before changing it. If the test exposes an implementation bug, preserve the assertion and fix the code rather than making the test pass by removing the check.

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  • Do not delete assertions, skip failing tests, or change expected values simply to obtain a green result.
  • After focused tests pass, run the related suite to look for interactions with surrounding behavior.

5. Inspect the code and test quality yourself

Read the diff and test output rather than relying on an agent’s summary. Confirm that every assertion corresponds to a requirement and that mocks do not replace the behavior the test is supposed to exercise. Inspect generated code for edge cases, error handling, and assumptions that tests may not cover. Check for common security problems such as injection risks, hardcoded secrets, and missing input validation. VS Code’s code-review guidance also emphasizes reviewing generated changes rather than accepting them unexamined.

6. Treat AI pull-request review as an extra signal

GitHub Copilot can provide feedback on a pull request, but GitHub cautions that it may miss issues and may make mistakes. Its review is not a substitute for tests or human review, and by default it does not count toward required pull-request approvals. GitHub’s Copilot code review documentation describes these limits and settings.

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Check whether the repository is configured to review new pushes. A review may not run again automatically after you push additional changes unless you request a new one or configure reviews on new pushes. If you use Copilot review, describe it as supplemental feedback, not evidence that the change is correct.

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7. Report validation honestly

In the pull-request description, state which commands you ran and whether they passed or failed. Include skipped tests and checks you could not run, with a brief reason. Distinguish a successful test run from an unverified check, and mention AI review only as an additional review signal. This gives reviewers a reliable account of what the change has—and has not—been checked against.

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