Structure-Based Testing Criteria and Testing Case Generation for Deep Learning Systems

Deep neural networks DNN are currently the basis of many modern AI ap-plications has been widely applied in various domains. As more safety-oriented fields autonomous driving, medical diagnosis, etc. begin to use DNN, people have put forward new requirements for DNN. Not only the ac-curacy of DNN and other objective indicators be excellent, but also have ro-bustness and ability to handle various corner cases. It is important to test the adequacy of the deep neural network model, design appropriate evaluation indicators, build a complete test evaluation system. However, deep neural network computing like a black box, a slight disturbance to the input may cause errors in the final output of the model. Therefore, it is important to test the adequacy of the deep neural network model, design appropriate eval-uation indicators, build a complete test evaluation system. We prove that there are differences in the internal structure of neural networks for different types of input. Based on this discovery, we proposed Multi-Layer test criteria based on the neural network structure. To quantify and analyze the changes in the internal structure of neural networks under different types of input, this paper pro poses an algorithm for mapping the deep neural network to tree structure data. Finally, a Multi-Layer test criteria based on the neural network structure is proposed to guide the generation of test cases, which can generate high-quality test cases.