AI code review found nothing. What does that prove?

Start with what the reviewer could see

A review of a file-upload function may not include the storage policy or reverse-proxy configuration. If those were outside its context, silence about public file access is not a test result.

GitHub documents incomplete detection and false positives among the limitations of its AI analysis. Keep the commit, reviewed files and tool output. Do not turn an empty comment thread into a security approval.

An alert still needs evidence

For a suspected flaw, ask for the affected path, necessary conditions and a safe way to verify it. A plausible explanation without those details is a lead. In an authorized test environment, record expected and observed behavior using synthetic data.

False positives need a reason too. Preserve why an alert was dismissed and what would invalidate that decision. Another deployment or a changed setting may make the old conclusion unusable.

A proposed fix is not a verified fix

Read the patch before merging it. Run the relevant regression checks and confirm which version reached the target environment. AI can help draft the work; the deployed behavior remains the evidence.

Separate code review from external checks when commissioning work. Mingxi's standard public check does not include source audit or business-logic testing. A clean result applies only to its agreed scope and date.

Practical checklist

• Keep the reviewed commit and context.
• Separate suspected issues from verified findings.
• Document false-positive decisions.
• Verify the deployed fix, not just the patch.

A common question

Can I ask another AI to confirm the first review?

A second opinion may raise useful questions, but agreement between models is not independent runtime evidence. Verify the relevant behavior within an authorized scope.