Prom detects deployment-time data drift in ML models for code analysis and optimization by combining weighted conformal prediction with ensemble voting, and shows that retraining on fewer than 5% of flagged samples restores near-design-time performance.
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Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization
Prom detects deployment-time data drift in ML models for code analysis and optimization by combining weighted conformal prediction with ensemble voting, and shows that retraining on fewer than 5% of flagged samples restores near-design-time performance.