How to assess AI code review precision vs cost

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What you are probably seeing

Your engineers spend significant time dismissing alerts that are not actual errors, which creates friction and slows down the review process.

You notice that high volume of flagged issues does not correlate with high quality, causing frustration and a decline in the team’s belief in the automated feedback.

Why it happens

Many tools prioritize recall to catch more bugs, which inevitably increases the noise from false positives and dilutes the signal of critical issues.

Focusing solely on cost per detected bug ignores the hidden labor cost of triaging incorrect warnings, which is often higher than the licensing fee.

What changed recently

Step by step

1. Define a clear definition of a false positive based on the specific coding standards your team follows.

2. Run a controlled test on a subset of your codebase where you already know the correct answers to measure accuracy.

3. Calculate the precision and false positive rate separately from the cost per true positive found.

4. Survey your development team to gauge their current trust and frustration levels regarding the tool’s alerts.

5. Compare these metrics across candidates to choose the tool that offers the best balance of accuracy and ease of use.

Ways to go further

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