AI in Code Reviews Developers Retain Control Over Merges

GitHub's pull request (PR) system, introduced in 2008, embedded accountability into software development by requiring peer approval before merging code. Seventeen years later, despite the rise of AI-powered coding tools, the PR remains the gatekeeper of code quality and governance. The question of accountability for AI-generated code is clear: the developer who hits 'Merge' is ultimately responsible.

Key Findings from GitHub Copilot's Code Review Capabilities

1. No Special Treatment for AI: Model-generated code is scrutinized as rigorously as human-written code.
2. Self-Reviews Improve Efficiency: Developers using GitHub Copilot for self-reviews reduce trivial feedback by ~30%.
3. AI Can't Replace Human Judgment: Trade-offs in architecture, mentorship, and ethics require developer input.

What AI Can (and Can't) Do in Code Reviews

• Strengths:

  • Mechanical scanning (typos, unused arguments)
  • Pattern matching (SQL injection risks, unawaited promises)
  • Consistency checks (naming conventions)

• Limitations:

  • Architectural decisions (service splitting, caching strategies)
  • Mentorship (explaining pattern rationale)
  • Ethical considerations ("Should we build this?")

Best Practices for AI-Assisted Code Reviews

• Pre-PR Self-Review: Use Copilot in your IDE to catch obvious issues early.
• Ownership: "If an AI agent writes code, it's on me to clean it up before my name shows up in git blame." — Jon Wiggins, Respondology
• Automate CI Checks: Maintain rigorous testing, scanning, and linting pipelines.
• Define Roles: Use AI for repetitive tasks; reserve human judgment for complex decisions.

Implementation Tips

• Document guidelines for AI use in reviews (customize Copilot's behavior).
• Update processes as AI capabilities evolve.
• Foster team discussions about AI's strengths and limitations.