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This assumes that human reviews also catch these which is DEFINITELY not the case either.

As long as you have good pipelines, linters, a careful suite of tests at different levels like unit, integration, e2e and if you can test things in an acceptable like environment then human code reviews offer very very little benefit…

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Your never going to catch 100% of issues, human or AI review, but I've found that in code reviews a lot of the benefit is when people ask questions about the code being reviewed and have a discussion on it.

Is the AI tool going to ask why something was implemented in a way that might not match the requirement specs? Is it even going to know what the requirements are for the code or is it going to rubber stamp a review because the code looks reasonable?

If you think human code reviews offer very very little benefit then you probably aren't doing them right.

PR reviewing isn't really about finding bugs because the tests should be doing that - but are the tests good enough? Is the approach sound and aligned with the architecture? And does anyone else understand it apart from the author?

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