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What deterministic checks can—and cannot—tell you
A deterministic check is designed to give a repeatable result for defined inputs and conditions. A unit test, for example, checks whether a function produces an expected outcome for a particular input. A static rule can flag a pattern without running the program. These checks make review more concrete, but real results can still vary with environment, flaky tests, or external services.
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Verification is therefore a set of complementary techniques, not a single pass/fail gate. The National Institute of Standards and Technology (NIST) recommends 11 broadly applicable minimum verification techniques, including automated testing, static code scanning, secret detection, black-box and structural test cases, historical test cases, fuzzing, applicable web application scanners, and attention to included libraries and services. NISTIR 8397, published in 2021, explicitly says it does not cover the totality of software verification. Read NISTIR 8397.
A practical verification workflow for AI-assisted changes
- Write down expected behavior. Before prompting an assistant, describe observable requirements: what should happen, what should not happen, and how errors or boundary cases should behave. This gives implementation and review a target that is independent of the generated code.
- Encode requirements as tests. Keep relevant existing tests and add cases for important new behavior. After the change, run the repository’s established test suite in its documented environment. A passing suite only checks the behavior those tests specify.
- Run security and code-quality checks. Use the project’s static analysis and secret detection, along with relevant dependency and security checks. Add fuzzing or a web application scanner where the software’s risk and exposure warrant them.
- Inspect the diff and test coverage. Review what changed, then check whether the tests genuinely exercise the requirement, including failure paths and boundaries. If a test simply repeats the assumptions in the generated implementation, both can miss the same mistake.
- Respond to failures without gaming the gate. A failed check is information. Fix the implementation when it violates a valid requirement; revise a test only when the requirement itself was wrong. Making a check green by weakening it can remove the very evidence it was meant to provide.
- Keep a human review. A reviewer still needs to assess intent, architectural fit, and risks that automated checks do not encode.
What each check contributes
| Check | Useful for detecting | What remains outside its coverage |
|---|---|---|
| Unit and integration tests | Incorrect behavior for the inputs, interactions, and outcomes the tests specify. | Requirements the tests omit, untested edge cases, and defects that do not affect the checked outcomes. |
| Static analysis and code scanning | Patterns associated with defects or security issues, without relying only on executing the changed path. | Every possible defect or whether the change matches product intent. |
| Secret detection | Credentials or other sensitive values exposed in the code being scanned. | Secrets outside the scanner’s coverage or values it does not recognize. |
| Dependency and included-code review | Risks associated with libraries, services, and other included components. | Whether the application’s own behavior is correct or every component risk has been eliminated. |
| Fuzzing and web application scanning | Some failures or vulnerabilities exposed by generated inputs or applicable scanning against an application. | All possible inputs, deployment conditions, and vulnerabilities. |
| Human review | Whether the change appears to satisfy intent and fit the architecture and risk context. | A guarantee of correctness; reviewers also have limited time and can miss defects. |
These methods work best as layers. A unit test may catch a behavioral regression quickly; static and security checks can flag problems that a behavior test does not exercise; review can question whether the checks represent the right requirement at all.
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What published AI-code evidence does—and does not—show
GitHub’s company-published study report describes a randomized study of experienced Python developers working on a fictional restaurant-review web-server task. It received 202 valid submissions: 104 with Copilot and 98 without. The task was evaluated using 10 unit tests and expert review. GitHub reported that participants with Copilot were 53.2% more likely to pass all 10 tests. That figure applies to this study’s task and setup; it is not a general estimate that AI-generated code is safer or better across projects. See the study and methodology.
GitHub’s documentation provides a useful example of evaluating proposed changes against existing checks. Its Autofix evaluation merges suggested changes unedited before running code scanning and repository unit tests, then examines whether the original alert is fixed, whether new alerts or syntax problems appear, and whether test outputs change. GitHub also cautions: “Developers must evaluate each suggestion and verify it maintains the codebase’s intended behavior.” Read GitHub’s security and quality AI feature documentation.
That advice is broader than any one product: run the change through the project’s checks, and review whether those checks represent the intended behavior. GitHub’s documentation also describes multilayered evaluation of inline suggestions, including test suites, controlled user segments, and code-vulnerability risk assessment. Such vendor methods are examples of verification practice, not independent proof that every generated change is safe. Read GitHub’s inline-suggestions documentation.
Where AI-specific secure-development guidance fits
NIST SP 800-218A, published in 2024, is an AI-specific SSDF Community Profile. It supplements SSDF 1.1 for generative AI and dual-use foundation model development. It is relevant context for organizations developing those systems, but it is not a checklist specifically for everyday application coding assisted by AI. Read NIST SP 800-218A.
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