The evidence behind drift.

BACKGROUND: Existing research on AI-generated code quality has focused primarily on aggregate metrics that measure how often drift occurs without accounting for its impact or remediation cost. This study argues that frequency alone is an insufficient and potentially misleading measure of production readiness risk. 

METHODS: Using ReWeaver, a multi-dimensional deterministic code drift scanning engine, we compared five leading AI coding tools against a human-authored reference across eight dimensions of production readiness, calculating both drift frequency and the Production Drift Ratio (PDR), a severity-weighted metric that converts drift into estimated remediation cost. 

RESULTS: Drift was present across all code tested. AI tools produced higher drift frequency than the human baseline on five of eight dimensions. However, differences were not statistically significant. PDR scores diverged substantially from frequency rankings across every dimension reaching statistical significance (z = −5.43, p < .001). The divergence was most pronounced in the Security & Privacy dimension, where AI tools produced three times the human drift frequency but a PDR 22x higher. No tool performed uniformly well across all dimensions.

CONCLUSION: Frequency tallies what went wrong. The PDR quantifies what it will cost to fix it.