CHEAPUNO guide

How to test if AI code reviews are consistent for security

Run back-to-back AI review sessions to see if findings repeat, so you know which results are safe to act on.

CHEAPUNO EditorialSources checked 8 10 月, 2026

What you are probably seeing

You run the same pull request through an AI review tool twice and get different lists of security warnings each time.

Your team debates whether to fix the new alerts, but you cannot tell if they are real bugs or just random model noise.

You notice that some maintainability tips disappear on the second pass, while new ones appear that were not there before.

Why it happens

Large language models use sampling temperatures that introduce randomness into their output, causing them to trade breadth for consistency in reviews.

The specific skills and prompts used for code review are often configurable, which means the AI’s behavior changes based on settings that are hard to audit.

What changed recently

Step by step

1. Create a standard set of test code snippets that contain known security flaws and style issues.

2. Run these snippets through your chosen AI review tool two times in a row without changing any settings.

3. Compare the two outputs side by side to identify which findings appear in both sessions and which are unique.

4. Discard any alerts that only appear once, as they are likely to be hallucinations or low-confidence guesses.

5. Document the consistent findings as your baseline for what the tool reliably detects and ignores.

6. Adjust your review process to manually verify any new, non-repeating alerts before pushing code.

Ways to go further

Affiliate links: CHEAPUNO may earn a commission if you buy through them, at no extra cost to you.