AI-Generated Code Review: 8-Point Checklist + Tools (2027)

AI-generated code requires especially rigorous review because polished appearance can conceal defects, insecure authorization, invented dependencies, deprecated APIs, weakened tests, and scope creep, while studies cited in the text suggest that functional AI solutions often remain insecure and may introduce more defects than human-written changes. The recommended process begins with deterministic CI checks for builds, warnings, tests, static security analysis, dependencies, and secrets, followed by AI review tools and a final human comparison of the diff against the original ticket. An eight-point checklist covers compilation, intact and meaningful tests, valid packages and APIs, current framework practices, secure input handling and access control, production-scale performance, consistency with existing codebase conventions, and strict adherence to requested scope. Teams are advised to split oversized changes, prioritize high-risk paths such as authentication and data access, inspect lockfile changes, reject and re-prompt when errors are systemic or tests are weakened, and retain human accountability for production approvals. The text also compares rule-based scanners and AI review products, emphasizing their differing capabilities, costs, and limitations, and argues that organizational controls such as named owners, required security gates, audit trails, and platform-specific publishing policies are necessary to govern AI-generated code effectively.

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