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AI coding tools can produce code quickly, but speed does not establish that a change behaves as intended or is safe to deploy. Verify AI-generated code with complementary checks: define observable requirements, run tests, scan for defects and secrets, inspect the diff, and have a person review decisions that automated checks do not capture.
What deterministic checks can—and cannot—tell you
A deterministic check has explicit inputs and an expected outcome, so it can be repeated and compared: for example, a unit test that expects a particular result for a particular input, or a static rule that flags a prohibited pattern. In practice, repeatability can be affected by flaky tests, environment differences, and external services, so not every check is perfectly deterministic.
Verification is not a single test that proves a program correct. Tests provide evidence about the behaviors they exercise; static analysis, secret detection, security checks, and human review address different risks. A passing suite does not prove every unstated requirement or untested case.
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The National Institute of Standards and Technology (NIST) published NISTIR 8397 on October 6, 2021. It recommends 11 broadly applicable verification techniques, including automated testing, static code scanning, secret detection, black-box and structural test cases, historical test cases, fuzzing, and applicable web application scanners. The report also calls attention to included code, such as libraries and services. NIST describes these as minimum recommendations, not a complete account of software verification.
#1 Best Overall
A practical verification workflow for AI-assisted changes
1. Define behavior before generating code
Write down what the change must do in terms that can be observed: inputs, expected outputs, relevant error behavior, and important boundaries. This gives you a standard for judging both the implementation and its tests, rather than relying on the generated code to define what “correct” means.
2. Encode requirements in tests
Keep existing tests and add focused cases for the behavior you specified. Include meaningful error cases and boundaries, not just the ordinary success path. A test can still miss a mistaken assumption if it merely reproduces the generated implementation’s logic; check that the expected result comes from the requirement.
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3. Run the project’s established checks
Run the repository’s test suite after the change, along with its established static analysis and relevant security and dependency checks. Add secret detection where it is not already part of the workflow. Fuzzing or web application scanning may be appropriate for the application and risk involved; they are not substitutes for the rest of the checks.
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Read the actual changes, not only the assistant’s explanation. Confirm that the diff addresses the requirement, that the tests exercise the intended behavior, and that unrelated code or configuration has not changed unexpectedly. A green test result is useful only to the extent that the tests cover the relevant behavior.
5. Respond to failures without chasing a green build
Treat a failed check as information. Fix the code when it violates a valid requirement; revise a test only when the requirement or expected result was itself wrong. Changing a test merely to make the run pass weakens the evidence the test was meant to provide.
6. Keep human review in the loop
Automated checks cannot fully encode intent, architecture, or every project-specific risk. A reviewer should assess whether the implementation belongs in the codebase, preserves intended behavior, and handles risks the checks do not cover.
Which checks catch which risks?
| Check | Useful for detecting | What remains outside its coverage |
|---|---|---|
| Unit and other automated tests | Behavioral failures for the inputs and outcomes the tests specify. | Unspecified requirements, untested cases, and defects shared by the implementation and an incorrect test assumption. |
| Static code analysis | Patterns and defects covered by the configured rules, without requiring the code to run. | Requirements or risks not represented by those rules. |
| Secret detection | Credentials or other sensitive values identified by the detector. | Secrets the detector does not recognize, and broader correctness or security questions. |
| Security and dependency checks | Relevant vulnerabilities or issues identified in the code, dependencies, or configured checks. | Unknown issues and risks outside the tools’ coverage or configuration. |
| Fuzzing and web application scanners | Some failures exposed by generated inputs or scanner-specific analysis, when appropriate to the application. | All possible inputs, conditions, and application-specific requirements. |
| Human review | Intent, architecture, and contextual risks that automated rules may not encode. | It is not a replacement for repeatable tests and technical checks. |
Checks can run locally for fast feedback and in continuous integration (CI) to apply the repository’s established process to proposed changes. A local pass is not evidence that a different CI environment or an external service will behave identically; investigate environment-dependent results rather than treating them as interchangeable.
How to interpret claims that AI improves code quality
Results depend on the task, participants, and how quality is measured. GitHub’s company-published study report describes a randomized comparison involving 243 experienced Python developers; 202 valid submissions were included (104 with Copilot and 98 without). Participants worked on a fictional restaurant-review web-server task assessed with 10 unit tests and expert review. The report says participants using Copilot were 53.2% more likely to pass all 10 tests. That is a study-specific result from one task, not a general estimate that AI-generated code is 53.2% better or safer. The GitHub study report describes its methodology and results.
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The evidence does not support a blanket claim that AI-written code is always better or always worse. A result on one task cannot establish how a different codebase, requirement, or risk will fare. The practical response is to verify the specific change with checks that match its expected behavior and security needs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What official guidance says about AI-assisted code
GitHub’s documentation for its security and quality AI features says: “Developers must evaluate each suggestion and verify it maintains the codebase’s intended behavior.” Its Autofix evaluation process illustrates a layered check: GitHub merges suggested changes unedited, then runs code scanning and repository unit tests and examines whether the original alert is fixed, new alerts or syntax problems appear, and test outputs change. This describes an evaluation method, not a guarantee that every suggestion is safe.
NIST’s SP 800-218A, published July 26, 2024, supplements the Secure Software Development Framework (SSDF) 1.1 for generative AI and dual-use foundation model development. It is useful context for secure development involving those systems, but it is not a checklist specifically for everyday AI-assisted application coding.
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