Single Pass, Half the Results: Why AI Code Review Demands Multiple Iterations

AI code reviewers operate probabilistically rather than deterministically, meaning each run produces different results due to inherent randomness and anchoring effects. Research shows that multiple passes with self-aggregation strategies can increase bug detection by 118%, proving that single reviews catch less than half of actual issues. This fundamentally changes how development teams should approach AI-assisted code quality, shifting from one-off reviews to iterative multi-pass strategies with specialized analysis.