AI-assisted code review can compare a focused diff with requirements, architecture, project instructions, tests, security expectations, and code-quality standards. It can classify findings by severity and suggest concrete corrections.
Review quality depends on relevant context and independent verification. Models can miss defects or produce plausible false alarms, so must-fix findings should cite specific evidence and human reviewers remain responsible for approval and merge decisions.
ELI5
AI-assisted code review uses an AI model to inspect software changes and suggest possible problems. It can help a reviewer notice risky logic, missing tests, confusing structure, or security issues, but its findings still need verification.
For example, the model may flag that a new account endpoint forgets an authorization check. A developer should inspect the surrounding code and run focused tests before deciding whether the warning is correct and how to fix it.
What should AI-assisted code review inspect?
It should inspect requirements, correctness, security, data handling, architecture, tests, errors, performance, maintainability, and project conventions.
Can AI approve its own code change?
It can provide evidence and suggestions, but consequential approval should remain independently verified and follow the project's ownership rules.
