How higher order mutant testing performs for deep learning models: A fine-grained evaluation of test effectiveness and efficiency improved from second-order mutant-classification tuples

Abstract

Given the prevalence of Deep Learning (DL) models in daily life, it is crucial to guarantee their reliability by DL testing. Recently, researchers have adapted mutation testing into DL testing to measure the test power of test sets. The bottleneck of DL mutation testing is the expensive costs of generating a large number of mutants.