How can AI be used to perform code reviews?

How can AI be used to perform code reviews?

AI can act as an additional code reviewer by examining a change and asking whether it meets its requirements, fits the surrounding system, and introduces defects. Its findings are leads to investigate. A plausible explanation is not sufficient evidence that an issue exists.

Provide the change and its context

Give the reviewer the diff, relevant surrounding files, acceptance criteria, and architectural constraints. For a wedding-event update endpoint, include the rules describing which organizers or collaborators may edit that event.

Ask for actionable findings with an affected location, a concrete failure scenario, and the expected behavior. This is more useful than a long list of general style preferences.

State the intended scope so the reviewer can distinguish a regression from unrelated improvement work.

Review behavior across boundaries

Useful review areas include input validation, server-side authorization, data ownership, asynchronous failures, duplicate submissions, and interface states. The reviewer should follow information across layers instead of assessing each file in isolation.

For example, hiding an edit button from guests does not enforce event-editing permissions. The server must independently check the caller’s access before updating the record.

AI may also notice that a test exercises the success path but omits a cross-wedding access attempt.

Verify findings before applying fixes

Reproduce a reported problem or trace the relevant execution path. Some findings rely on incorrect assumptions about framework behavior, existing helpers, or product requirements.

Implement justified fixes, review their impact, and run appropriate checks again. If a finding is rejected, record the reason when it may otherwise recur.

AI review complements developer judgment, automated checks, and security practices. It does not guarantee that a change is safe or correct.