Namith Nishal Raphae

RiskBlend: A Multi-Signal Framework for Test Input Prioritization in Machine Learning Regression Testing

RiskBlend is proposed, a classifier-agnostic prioritization framework that combines four complementary risk signals: historical failure patterns, prediction shift, decision-boundary shift, and neighborhood change that achieves the highest average APFD in all 80 dataset-classifier-scenario combinations.