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1. Make the specification testable
Choose the authoritative version of the specification and identify which requirements are in scope. For each one, write down the conditions under which it applies, the input, the expected result or side effect, and what an observer can check to decide whether it passed.
Vague requirements need clarification before they can be verified precisely. For example, “handles errors” should specify which errors can occur and what the software must do; “fast” needs a measurable limit and defined conditions; “secure” needs concrete security properties. Ask the specification owner to resolve these terms, or record them as open requirements rather than treating an assumption as an acceptance criterion.
Assign each requirement an ID. That creates a traceable link between what was requested, the test cases intended to verify it, and the results you report.
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2. Derive black-box tests from the requirements
A black-box test checks observable behavior without relying on how the code is implemented. NIST describes this approach as a way to test functional specifications and requirements. For each requirement, build test cases that would distinguish compliant behavior from a plausible but incorrect implementation.
A test case should record its setup, input, expected result, and failure condition. Include the ordinary case, but do not stop there: test invalid inputs, boundaries, relevant combinations, and behavior the program must not exhibit. NIST’s minimum verification guidance identifies functional requirements, invalid inputs, overload or denial-of-service attempts, input boundaries, and input combinations as areas to consider.
| Test angle | What to specify | Example question |
|---|---|---|
| Normal behavior | A representative valid input and the required output or side effect | Does a valid request produce the result the requirement calls for? |
| Invalid input | Inputs that violate stated constraints and the expected rejection or recovery | Is malformed input rejected without an unintended change? |
| Boundary | Values at, just below, or just above a stated limit | Does the behavior change correctly at the documented maximum? |
| Combination | Inputs or conditions that can interact | Does a valid request still behave correctly when another relevant condition is present? |
| Prohibited behavior | An observable action that must not occur | Does an unauthorized request leave protected data unchanged? |
Choose cases based on the actual requirements and risks; the table is a set of prompts, not a substitute for defining expected outcomes. If a requirement does not establish an expected result for a case, resolve the ambiguity instead of inventing an oracle.
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3. Keep expected results independent of the generated code
Expected outcomes should come from the specification, examples approved by the product or domain owner, or independently established invariants. If the same AI workflow generated the implementation and its tests, the tests may share the implementation’s mistaken assumptions. Treat AI-written tests as hypotheses to review, not independent proof.
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OWASP’s Secure Coding with AI guidance warns about test changes that can make a failing suite appear to pass. Review proposed tests and edits for:
- Deleted failing tests or removed cases that previously covered a requirement.
- Assertions weakened so that incorrect results still pass.
- Mocks that replace the unit under test rather than isolating an external dependency.
- Expected results that encode the implementation’s behavior even when it conflicts with the specification.
When a test fails, investigate whether the implementation, the test, or the requirement is wrong. Do not resolve the failure by changing the assertion until the expected behavior has been checked against an independent source.
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4. Add checks that complement acceptance tests
Requirement-based tests establish whether selected inputs produce the required observable behavior. They do not necessarily exercise every important branch or protect against defects found previously. NISTIR 8397 recommends complementary verification techniques, including structural tests informed by the implementation, historical tests, fuzzing, automated testing, static scanning, and attention to dependencies.
Structural tests
Inspect the implementation and use code structure or coverage information to identify branches and paths the black-box suite has not exercised. These tests can reveal gaps in test coverage, but they should not replace requirement-based checks: implementation details do not define what the software is supposed to do.
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Regression tests
When a defect is found, keep a test that would catch it if it returns. Historical tests protect against reintroducing known bugs during later AI-assisted edits or refactoring.
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Fuzzing and property-based tests
For large input spaces, fuzzing or property-based testing can explore cases that are impractical to enumerate manually. Define useful invariants or safety properties first; generating many inputs without a meaningful oracle does not establish correctness.
Static analysis and dependencies
Use static scanning to look for code patterns and known issue classes, and review packages and dependencies used by the generated code. These checks complement behavioral tests; a clean scan does not establish that a requirement is met.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Scale security testing to the code’s risk
For security-sensitive code, identify important assets and trust boundaries, then test the security requirements and likely abuse cases—not just ordinary user flows. NIST SP 800-218A, a 2024 secure-development profile for generative AI and dual-use foundation models, describes executable-code testing to find vulnerabilities and verify security requirements. It lists possible approaches including unit, integration, penetration, red-team, use-case, and adversarial testing.
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OWASP AISVS Appendix C, which addresses AI for code generation, calls for qualified human review and automated security testing. It identifies input validation, authorization, and deserialization safety as candidates for targeted fuzzing or property-based tests. Apply deeper dynamic, web-application, or penetration testing when exposure and potential consequences warrant it; the appropriate mix depends on the system, not merely on whether AI wrote the code.
NISTIR 8397 provides general developer verification guidance rather than AI-specific evidence. NIST SP 800-218A addresses secure development for generative AI and dual-use foundation models. OWASP AISVS 1.0, released in June 2026, provides AI-specific security verification requirements and complements rather than replaces general application and infrastructure verification. Check the current OWASP standard and appendix text when applying them because standards can evolve.
6. Report what was verified—and what was not
For each requirement ID, record the test IDs and results, the implementation and environment versions, the checks that ran, failures, uncovered cases, and any human review. Keep unresolved ambiguities visible rather than marking them as passed.
A precise report says that the implementation passed the listed checks under the stated conditions. Passing tests supports a bounded claim about the behavior exercised; it does not establish that the specification is complete or that every untested behavior is correct. NIST’s and OWASP’s guidance supports layered verification, not a blanket guarantee of correctness.
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Sources and further guidance
- NISTIR 8397: Guidelines on Minimum Standards for Developer Verification of Software
- NIST minimum standards for developer verification
- NIST SP 800-218A: Secure Software Development Practices for Generative AI and Dual-Use Foundation Models
- OWASP AI Security Verification Standard (AISVS)
- OWASP AISVS: AI for Code Generation appendix
- OWASP Secure Coding with AI Cheat Sheet
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