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Deep Neural Networks~(DNNs) have been widely deployed in software to addressvarious tasks~(e.g., autonomous driving, medical diagnosis). However, theycould also produce incorrect behaviors that result in financial losses and eventhreaten human safety. To reveal the incorrect behaviors in DNN and repairthem, DNN developers often collect rich unlabeled datasets from the naturalworld and label them to test the DNN models. However, properly labeling a largenumber of unlabeled datasets is a highly expensive and time-consuming task.  To address the above-mentioned problem, we propose NSS, Neuron Sensitivityguided test case Selection, which can reduce the labeling time by selectingvaluable test cases from unlabeled datasets. NSS leverages the internalneuron's information induced by test cases to select valuable test cases, whichhave high confidence in causing the model to behave incorrectly. We evaluateNSS with four widely used datasets and four well-designed DNN models comparedto SOTA baseline methods. The results show that NSS performs well in assessingthe test cases' probability of fault triggering and model improvementcapabilities. Specifically, compared with baseline approaches, NSS obtains ahigher fault detection rate~(e.g., when selecting 5\% test case from theunlabeled dataset in MNIST \& LeNet1 experiment, NSS can obtain 81.8\% faultdetection rate, 20\% higher than baselines).

Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing