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To know whether an AI coding assistant broke your code, compare the change with the behavior your project must preserve, then run relevant tests and inspect what actually ran. A passing test suite is evidence—not proof—if it missed the changed code, skipped an important check, or no longer asserts the behavior that matters.

What counts as a regression?

A regression is a change that breaks behavior the project previously relied on. It may be obvious, such as a failing test, or subtle: a changed default, a different error response, a lost validation boundary, an unexpected side effect, or a public interface that no longer works for its callers.

AI-generated code can look valid while misunderstanding intent, and AI-generated tests may miss scenarios. GitHub recommends reviewing and testing generated code rather than assuming it is correct (GitHub Copilot code completion: responsible use). The same standard applies to an assistant’s claim that it tested a change: verify the command and its output.

How do I know AI didn’t break my code?

Use a verification loop that starts with the behavior to preserve, not with the assistant’s summary. The loop is: define the contract, establish a baseline, keep the change reviewable, run relevant checks, investigate failures, inspect test quality and the diff, then decide whether the evidence is enough to merge.

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1. Define the behavior to preserve

Before editing, write down what callers and users rely on: accepted inputs, defaults, validation boundaries, return values and response shape, ordering, error behavior, side effects, and public interfaces. If the change is meant to preserve behavior, separate that work from new features or unrelated cleanup. Microsoft’s Visual Studio Code refactoring guide recommends tracing existing behavior and known callers when the contract is unclear (Refactor code without changing behavior).

Requirements—not merely the current implementation—should define what tests expect. Otherwise, a test written against existing behavior could preserve a pre-existing bug instead of documenting the intended contract.

2. Establish a baseline and add missing regression tests

Run the relevant existing tests before the implementation changes. Record the exact commands and results so you can distinguish pre-existing failures from new ones. If coverage is missing, add tests for the agreed behavior first: valid and invalid inputs, boundaries, defaults, and observable results for affected callers.

For a refactor, keep a Git baseline and make the proposed scope small enough to review. Microsoft’s VS Code guide recommends asking the coding agent to identify relevant tests and propose a plan, then inspecting the scope and commands before execution. A prompt can guide the assistant; it cannot guarantee that the change stays within scope.

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3. Run focused tests, then related tests

Start with the smallest test selection that exercises the changed behavior. It is usually faster to diagnose a focused failure than a failure in a large suite. Then run related tests to check interactions. Record what ran, including pass/fail counts and skips; treat checks that did not run as unverified. Microsoft’s VS Code testing guide puts it plainly: “Treat tests that weren’t run as unverified” (Test existing code with AI).

Check runner output and the environment or configuration used. A coding assistant’s report is not a substitute for seeing the actual result; if it could not execute a command, run it yourself where possible.

4. Investigate failures instead of chasing a green result

Classify a failure before changing code or tests. It may be a setup problem, an incorrect expectation, or an implementation defect. A regression test that reveals a defect should remain in place while you consider the implementation fix separately.

Do not accept deleted assertions, skipped tests, or changed expected values solely because they make the suite pass. If an expectation truly needs to change, tie that change to the agreed contract and review it as carefully as the implementation.

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5. Review what the tests prove and what the diff changed

A passing test matters only if it exercises the behavior at issue and asserts the right outcome. Check that tests cover boundary and error cases and do not depend accidentally on test order, shared state, timing, or live services. Mocks can conceal a gap if they replace the behavior the test is supposed to exercise.

Review the diff for deleted or weakened tests, unrelated files, and changes to callers or contracts. Microsoft’s refactoring guide cautions that “a cleaner-looking diff doesn’t prove that the behavior is preserved.” Read the source and test output rather than relying only on an AI-generated review or summary.

6. Add other checks when the project calls for them

Linting, type checks, security scans, integration tests, and end-to-end tests can provide additional evidence when they are part of the project’s workflow. Choose checks according to the repository’s architecture and risks: what changed behavior they exercise, whether they run in the relevant environment, whether they cover unit, integration, or end-to-end behavior, and whether mocks hide the real behavior. No single test level is enough for every change.

GitHub’s March 18, 2026 changelog says Copilot coding agent automatically runs project tests and a linter, and lists CodeQL, the GitHub Advisory Database, secret scanning, and Copilot code review among its validation tools (GitHub changelog, March 18, 2026). That is a product-specific feature description, not a guarantee for other assistants or every repository; GitHub says repository administrators can configure checks.

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The tests pass, but did the changed code actually get tested?

Look for evidence that the changed lines or behavior were exercised, not just a green overall status. Check whether the relevant tests ran, whether assertions target the agreed contract, and whether a mock or stub bypassed the code under review. If the suite does not cover the changed behavior, the result does not verify that behavior; add an appropriate check or record the gap before merging.

A 2026 arXiv preprint analyzing 4,882 agent-generated pull requests in the AIDev dataset—532 Java and 4,350 Python PRs from five coding agents—illustrates why this check matters, without establishing a rate for every repository or assistant (2026 arXiv preprint on test coverage in agent-generated pull requests):

Finding in the sampled PRs Scope and qualification
49.6% of PRs that changed code under test files included test changes AIDev agent-generated PR sample analyzed in the 2026 preprint; not a universal rate.
Existing tests covered 61.5% of changed executable lines in Java and 27.0% in Python Languages and AIDev PR sample analyzed in the 2026 preprint.
64.8% of sampled Python PRs had no changed line executed by any existing test Python PRs in the AIDev sample analyzed in the 2026 preprint.
Agent-written tests increased coverage in 35.9% of sampled Java and 22.5% of sampled Python Code + Tests PRs Java and Python Code + Tests PRs in the AIDev sample analyzed in the 2026 preprint.

These findings describe one study sample, not a prediction about your change. The useful takeaway for an individual review is to verify coverage for the changed behavior directly.

How should I treat AI code-review comments?

Evaluate each finding against the source, requirements, and tests. GitHub documents that Copilot code review can produce false positives and inaccurate suggestions, and that proposed fixes may be inaccurate or insecure (GitHub Copilot code review: responsible use). A review comment is a lead to investigate, not a verdict.

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Review scope can also be limited: GitHub’s Copilot code-review documentation lists dependency management files, logs, and SVGs among excluded file types (Using GitHub Copilot code review). Check the configured scope for the platform and version you use, and inspect files the automated review does not cover.

When is the change ready to merge?

Decide against the behavior contract, not just the final test badge. Merge when the relevant checks ran, their assertions meaningfully cover the changed behavior, and the diff has not weakened the checks or altered a contract unintentionally. If coverage is absent, a required check was skipped, mocks mask the behavior, or the change alters a contract, identify that verification gap and add the missing check or review before merging.

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