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cs.SE2026
ADEPT: A Unified Framework for Deep Learning Test Adequacy
Yidi Kao, Shawn Burnham, Tommi Rose Fahy +1
Over the past decade, many test adequacy metrics have been proposed for deep learning that characterize test dataset adequacy from different perspectives, e.g., neuron activation b…
cs.SE2026
Provably Lossless Acceleration of DNN Mutation Testing via Memoization
Ali Ghanbari, Ben Greenman, Sasan Tavakkol +1
Mutation analysis has recently reemerged in the context of deep neural networks (DNNs) as a promising, but notoriously costly, approach for assessing test dataset adequacy. Existin…