Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteTo know whether an AI coding assistant broke something, compare the change with the behavior your code is supposed to preserve, then run relevant tests and verify what actually ran. A passing test suite is evidence—not proof—especially if it misses the changed code, skips checks, or relies on mocks that hide the behavior under review.
What counts as a regression?
A regression is a change that breaks behavior callers or users previously relied on. In a refactor, the intended implementation may change while the contract stays the same. That contract can include accepted inputs, defaults, validation limits, return values and response shape, ordering, error behavior, side effects, and public interfaces.
Start by writing down the relevant expectations before editing. If the contract is unclear, trace existing behavior and known callers; Microsoft’s Visual Studio Code refactoring guide recommends doing this before asking an assistant to refactor. Keep new features and unrelated cleanup out of a behavior-preserving change so that a failure is easier to explain.
How do I test code changes made by an AI assistant?
1. Establish a baseline
Run the relevant existing tests before the change and record the exact commands, results, and skips. This tells you whether a failure already existed. If tests do not cover the behavior at risk, add regression tests for the agreed contract, including valid and invalid inputs, boundary values, defaults, and observable outcomes for affected callers.
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Write tests from requirements, not just from the current implementation. Otherwise, a test can accidentally enshrine a pre-existing bug as the behavior to preserve.
2. Keep the proposed change small
Ask the coding assistant to identify relevant test commands and propose a bounded plan, then inspect the scope and commands before execution. Break larger refactors into reviewable steps and keep a Git baseline so you can compare or recover the change. These are useful workflow safeguards, not guarantees that a prompt will constrain the assistant.
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3. Run focused tests, then related tests
Begin with the smallest test selection that exercises the changed behavior; focused feedback makes failures easier to isolate. Then run the related suite to catch interactions. Record the actual commands, pass/fail results, and skips. Microsoft’s Visual Studio Code guide to testing existing code with AI puts the rule plainly: “Treat tests that weren’t run as unverified.”
Do not rely on an assistant’s summary as proof of execution. Inspect runner output and the environment or configuration in which the tests ran. If execution was blocked, run the checks yourself or report that verification gap.
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4. Investigate failures instead of chasing a green result
Work out whether a failure comes from setup, an incorrect expectation, or a possible implementation bug. Do not accept a deleted assertion, skipped test, or altered expected value solely because it makes the suite pass. If a regression test exposes a defect, keep the test while considering the implementation fix separately.
5. Review the tests and the diff
Check that assertions match the agreed behavior and cover relevant boundary and error cases. Look for tests that depend unintentionally on execution order, shared state, timing, or live services. Mocks are useful, but make sure they do not replace the behavior the test is meant to exercise. Inspect the test runner’s output, not only the assistant’s account of it.
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Then compare the full diff with the baseline. Look for removed or weakened tests, unrelated file changes, and modifications to callers or public contracts. As Microsoft’s Visual Studio Code refactoring guide warns, “a cleaner-looking diff doesn’t prove that the behavior is preserved.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What additional checks should I run?
Use the checks already appropriate to your project: linting, type checks, security scans, integration tests, or end-to-end tests may add useful evidence. Choose based on the affected behavior and system architecture rather than assuming one test level is sufficient. For each check, consider what changed behavior it exercises, whether it ran in the needed environment and configuration, and whether it is repeatable in CI.
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Product-specific automation is not a universal guarantee. 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; repository administrators can configure checks. That describes this product’s feature set on that date, not what every coding assistant or repository runs. Check the configuration and actual results for your own project.
Why can passing tests still miss an AI regression?
A passing result says only that the checks that ran passed. It does not show that the changed behavior was exercised or that assertions captured the intended contract. GitHub cautions that AI-generated code may appear valid while still being semantically wrong or missing the developer’s intent, and that suggested tests may not cover every scenario. AI-written tests therefore need the same review as the implementation.
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 coverage deserves attention. In that sample, 49.6% of PRs that changed code under test files also included test changes. Existing tests covered 61.5% of changed executable lines in Java and 27.0% in Python; 64.8% of sampled Python PRs had no changed line executed by any existing test. Agent-written tests increased coverage in 35.9% of sampled Java and 22.5% of sampled Python Code + Tests PRs. These are findings for that dataset and language sample, not universal rates or a prediction for your repository. Read the 2026 arXiv preprint.
How should I evaluate AI code-review findings?
Treat review comments as leads to investigate, not verdicts. GitHub documents risks that Copilot code review can produce false positives and inaccurate suggestions; check each finding against the source, requirements, and tests before changing code. Review coverage also has boundaries: GitHub’s documentation lists dependency-management files, logs, and SVGs among file types Copilot code review does not cover. Check the configured review scope for the platform and version you use.
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When is the change ready to merge?
Decide against the contract you wrote down, not simply against a green status indicator. A green suite is meaningful only insofar as its assertions are relevant and the changed behavior actually ran. If coverage is missing, mocks conceal the behavior, a required check was skipped, or the diff changes a contract, close the gap with an appropriate test or review—or clearly leave the change unverified rather than treating it as proven safe.
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