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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteTest AI-generated code the way you would any consequential change: check it against the requirements, run the project’s build and tests, add independent cases for edge conditions, apply security checks suited to the system, and have a responsible person review the full change before it is merged. A passing test suite only shows that the tests it contains passed; it does not establish that the code is correct or secure.
1. Define the expected behavior and inspect the full change
Start with the task, acceptance criteria, design constraints, and conventions already used in the project. Before running checks, compare the implementation with what it was supposed to do. GitHub’s code review guidance recommends checking generated code against project intent and architecture.
- Read the complete diff, not only the lines the assistant says it changed.
- Check that the change satisfies the requirement without unrelated edits or unexpected behavior changes.
- Look closely at authentication, authorization, input handling, data access, and cryptographic operations when those areas are touched.
Useful review questions include: What functional tests are missing? What vulnerabilities could this code introduce? What edge cases might it not handle?
2. Build the project and run functional tests
Use the project’s normal build or compile command, then run its existing automated tests. Review new errors and warnings rather than treating a green exit status as the entire review.
#1 Best Overall
- Build or compile using the project’s documented process.
- Run the existing test suite and note failures, skipped tests, and relevant warnings.
- Add or update tests for the requirement, including boundary values, malformed input, failure paths, and integration behavior where applicable.
- Keep or add regression tests for defects that could recur. If a failing test was deleted or weakened, understand why before accepting the change.
NIST’s Recommended Minimum Standards for Vendor or Developer Verification (Testing) of Software Under Executive Order 14028 includes automated tests and historical tests among its verification techniques. Those checks are useful evidence about tested behavior, not a proof that untested paths work.
3. Challenge the tests, not just the implementation
AI-generated tests can share the implementation’s assumptions. Review whether each test asserts the requirement or merely confirms the code behaves as it was written. OWASP warns against treating AI-generated test suites as security evidence and advises human review of test changes in its AI security guidance.
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- Add cases the generating assistant did not write, particularly invalid, negative, adversarial, and boundary inputs.
- Check for deleted tests, weaker assertions, excessive mocking, and tests that enshrine incorrect behavior.
- For security-critical behavior—such as access control, input validation, or cryptography—seek independent review and tests that exercise misuse as well as normal use.
4. Apply security checks that fit the system
Combine methods because they look for different classes of problems. NIST’s verification guidance covers threat modeling, automated testing, static scanning, checks for hardcoded secrets, black-box and structural tests, fuzzing, web application scanners when applicable, and review of included code such as libraries and services.
- Threat modeling: Consider trust boundaries, sensitive data, attacker goals, and how the change affects them.
- Static analysis: Scan source for suspicious patterns and likely defects. Review and reproduce findings rather than treating a scan result as a verdict.
- Secret checks: Look for credentials, tokens, keys, or other sensitive values accidentally added to source or configuration.
- Black-box and structural tests: Exercise externally visible behavior and inspect relevant internal properties, choosing the approach appropriate to the system.
- Fuzzing: Where suitable, feed varied or malformed inputs to uncover crashes and unexpected behavior.
- Web application scanning: Use a scanner when the change belongs to an applicable web system, then triage its findings.
No single scanner or test technique establishes that code is vulnerability-free. Choose depth based on exposure, potential impact, architecture, and data sensitivity.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems5. Verify dependencies and generated configuration
Do not assume an assistant’s package recommendation is real, maintained, safe, or current. GitHub’s review guidance and OWASP’s AI security guidance support independently checking suggested dependencies.
- Confirm each new package exists in the intended package registry and that its name is not a lookalike.
- Review maintainers, release history, licensing, and the version being introduced.
- Audit dependency versions for known vulnerabilities; update or pin them through the project’s usual dependency process.
- Inspect generated build, CI, infrastructure, and deployment changes for widened access or weakened controls.
6. Review the agent’s trust boundaries and permissions
When an AI agent can read issues, pull requests, repository documentation, logs, changelogs, or tool responses, treat that material as potentially attacker-controlled input. OWASP’s AI security guidance discusses these agent risks; NIST’s Introduction — Secure Software Development, Security, and Operations (DevSecOps) Practices calls for governance, authorization controls, auditability, and human oversight. NIST states that “AI-based suggestions should be subject to rigorous scrutiny by human actors to prevent uncritical acceptance.”
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- Give the agent and its CI job only the permissions needed for the task.
- Keep production secrets out of untrusted workflows.
- Require explicit human approval for consequential code, dependency, configuration, or deployment changes.
- Ensure a named owner understands and accepts the final change rather than relying on the agent’s summary.
7. Keep review evidence and resolve findings
For a change others must assess, retain test and scan results and explain any accepted exceptions. Fix critical findings before release. NIST describes its verification techniques as recommendations and supplemental guidance—not a guarantee that a particular program is free of vulnerabilities.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much testing is enough?
There is no universal scanner or fixed test set that makes AI-generated code safe. Scale the work to the change: a low-impact internal helper may need a focused build, tests, and review, while code handling sensitive data or exposed to untrusted input warrants deeper security analysis, adversarial testing, dependency review, and scrutiny of permissions. Judge the evidence by what it actually covers and keep the person approving the merge accountable for gaps.
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