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Start with the claim the change is supposed to satisfy
Verification is more useful when each check is tied to a concrete claim about the software. A claim might be that a user without a particular permission cannot access a record, or that malformed input is rejected without corrupting stored data. The developer’s job is to identify what the change is meant to guarantee, choose checks that exercise those guarantees, examine what the checks do not cover, and record unresolved risk.
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This is a practical way to apply the verification techniques in NISTIR 8397, Guidelines on Minimum Standards for Developer Verification of Software, published October 6, 2021. NIST presents broadly applicable recommendations, not a complete account of software verification or a guarantee that any one set of checks proves correctness.
Choose checks that match the behavior and risk
Different methods examine different aspects of a change. A test can show what happened for a particular input and setup; a scanner can flag patterns associated with known classes of problems. Neither result automatically answers whether the change is safe for its intended use.
| Check | What it can help examine | What it does not establish by itself |
|---|---|---|
| Black-box tests | Observable behavior through the software’s interfaces, including expected and failure cases. | That untested inputs, paths, or interactions behave correctly. |
| Code-based structural tests | Internal paths or structures that may not be exercised by ordinary interface-level tests. | That the chosen paths represent every important requirement or real-world condition. |
| Historical tests | Whether earlier behavior covered by existing tests has changed unexpectedly. | That the old tests were complete or that new behavior is correct. |
| Fuzzing | How software handles unusual or generated inputs that may expose crashes or unexpected behavior. | That all input combinations have been tried or that the absence of a discovered failure proves safety. |
| Static code scanning | Patterns that may indicate common coding defects. | That flagged findings are actual vulnerabilities, or that unflagged code is free of defects. |
| Heuristic secrets checks | Possible hardcoded credentials or other sensitive values. | That every secret has been detected or that a flagged string is necessarily a live secret. |
| Threat modeling | Design-level threats, trust boundaries, and ways the system could be misused. | That the implementation actually enforces the intended mitigations. |
| Built-in checks and protections | Whether platform or framework safeguards relevant to the change are in place and used. | That the safeguards are configured correctly for this system or cover every risk. |
| Web application scanners | Potential issues in web applications that are within the scanner’s coverage. | That all application vulnerabilities have been found or that results require no human interpretation. |
| Dependency and service review | Included libraries, packages, and services that affect the change’s behavior or exposure. | That every dependency is appropriate, safe, or correctly configured merely because it is present. |
NISTIR 8397 recommends these techniques as a baseline. The appropriate combination depends on the system, the specific change, and the consequences of failure; the report explicitly does not cover the totality of software verification.
Make AI review one source of evidence, not the verdict
An AI reviewer can surface plausible defects, but a clean review does not show that the requirements are complete, relevant cases are tested, or the implementation is secure. GitHub’s Copilot responsible-use guidance says to verify its feedback and supplement it with careful human review. Its guidance also cautions that generated code can be syntactically correct without being secure.
Review a suggested finding as you would any other review comment: determine whether it applies to the actual code and requirements, then verify the concern with inspection or a suitable check. Likewise, do not treat an AI-generated explanation, test, or approval as independent confirmation just because it sounds confident. The output needs to be evaluated against the behavior and system it describes.
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Keep human approval attached to consequential actions
NIST’s DevSecOps reference model describes direct human supervision, including review and validation of AI-generated outputs in its initial phase. It says AI-generated corrective actions should not change software, configurations, or system state without review and approval through established processes. In practice, an automated suggestion may accelerate analysis, but the developer or approving team decides whether it is justified and whether the change is ready to ship.
NIST SP 800-218A, published in 2024, adds practices for developing AI models and dual-use foundation models across the software development life cycle. It augments SSDF version 1.1; it should not be mistaken for a universal code-review checklist for every team using a coding assistant.
A practical verification sequence
- State the intended behavior and constraints. Identify what should change, what must remain unchanged, and any security, compatibility, or operational boundaries that matter.
- Connect each important claim to a check. Choose tests or analysis that exercise the relevant behavior, including boundary and failure conditions where they matter.
- Inspect the change’s broader exposure. Consider design threats, common coding defects, possible hardcoded secrets, built-in protections, and included libraries, packages, or services.
- Run checks and interpret their limits. A passing test reports results for its cases; a scanner reports what its methods can detect. Investigate findings and identify important cases the checks did not cover.
- Record remaining uncertainty and make the approval decision. Decide whether the evidence is proportionate to the risk, whether unresolved issues are acceptable, and who is accountable for accepting them.
What evidence cannot replace
No general accuracy statistic for AI-generated code or AI code review is established by the sources cited here. NISTIR 8397 is verification guidance, and GitHub’s Copilot page is responsible-use documentation, not a broad accuracy study. A percentage from a narrow benchmark would not establish how reliably an AI system handles a different codebase, requirement, or failure consequence.
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The central question is therefore not whether AI wrote or reviewed the code. It is whether the evidence supports the specific claims that make this change acceptable—and whether a responsible human has judged that evidence and approved the risk.
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