Testing Adequacy of Convolutional Neural Network Based on Mutation Testing

It is difficult to apply traditional testing adequacy criteria when measuring the adequacy of convolutional neural network applications. However, only a small number of test cases applied to the CNN model can achieve neuron coverage of almost 100%, overturning the effectiveness of the neuronal coverage criteria. In this paper, we propose a model coverage criterion based on mutation testing for CNN, and applying model coverage criterion to a common CNN image classification models (LeNet-5). we focus on the testing accuracy of model. Experiments show that our method can find the local optimal model and play an important role in improving the testing adequacy of the set of models.