AI Code Review
The use of artificial intelligence and machine learning to automatically analyze code changes and provide feedback on quality, security, and best practices.
Definition
AI code review tools use large language models (LLMs) and specialized algorithms to understand code context and identify issues. Unlike traditional static analysis that matches patterns, AI reviewers can understand intent, suggest improvements, and catch subtle bugs. Modern AI code review tools like diffray use multi-agent architectures where specialized agents (security, performance, architecture) collaborate to reduce false positives.
Why It Matters
AI code review addresses the "review gap" — developers write code faster than humans can review it. Studies show AI code review can reduce review time by 50-70% while catching more bugs. However, not all AI reviewers are equal — single-agent tools often have 70%+ false positive rates, while multi-agent systems like diffray achieve 87% fewer false positives through cross-validation.
Example
A developer opens a PR and diffray's Security Agent detects a potential SQL injection, the Performance Agent flags an N+1 query, and the Architecture Agent suggests a better design pattern — all within 2 minutes.