Abstract: Deep neural network(DNN) testing requires a large amount of test data to ensure the quality of DNN.However,most test inputs lack annotation information,and annotating test inputs is costly.Therefore,in order to address the issue of annotation costs,researchers have proposed a test input prioritization approach to screen high priority test inputs for annotation.However,most prioritization methods are influenced by limited scenarios,such as difficulty in filtering out high confidence misclassified inputs.To address the above challenges,this paper applies differential testing technology to test input prioritization and proposes a test input prioritization method based on DNN model output differences(DeepDiff).DeepDiff first constructs a contrast model that has the same functionality as the original model,then calculates the output differences between the test inputs on the original model and the contrast model,and finally assigns higher priority to the test inputs with larger output differences.For empirical evidence,we conduct a study on four widely used datasets and the corresponding eight DNN models.Experimental results demonstrate that DeepDiff is 13.06% higher on average in effectiveness compared to the baseline approaches on the original test set and 39.69% higher on the mixed test set.

Key words: Deep neural network testing, Test input prioritization, Differential testing, Model output differences