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How to choose an AI code reviewer

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How do I choose an AI code reviewer?

Run each candidate on your own recent pull requests and compare how many real bugs it finds, how much of what it says is noise, what it costs at your team’s volume, and what happens to your code; published claims matter less than a trial on your code.

The criteria that matter

  1. Real bugs found (recall). Does it catch the defects you already know were in past pull requests?
  2. Noise (precision). What share of its comments are real problems? A reviewer people learn to ignore is worth nothing. See recall and precision.
  3. Price at your volume. Per seat or per review? Work out the cost for your pull requests per month, including large and follow-up commits.
  4. Spend control. Can you cap spend per day or month, and review only on request?
  5. Security coverage. What does it check, and what does it say it does not check?
  6. Pushback. When it is wrong, can you reply and have it reconsider, or does the comment just sit there?
  7. Data handling. How long is your code kept, who processes it, and is it deleted when you uninstall?
  8. Fit with your workflow. A check you can require, your own rules files, and an API for CI.

Questions to ask any vendor

Running a fair trial

  1. Pick 10 to 20 recent pull requests where you know what went wrong later.
  2. Run every candidate on the same pull requests, at the same commit.
  3. Count real findings, missed known bugs, and noise for each.
  4. Price each at your real monthly volume.

Where MergeSafe fits

You can run your own trial on the $10 every new organization gets. Start with Getting started.

Next: Benchmarks →