Deep neural networks (DNNs) have a wide range of applications, and software employing
them must be thoroughly tested, especially in safety-critical domains. However, traditional …

Deep neural networks (DNNs) have a wide range of applications, and software employing
them must be thoroughly tested, especially in safety-critical domains. However, traditional …

Deep Neural Networks (DNNs) have been extensively used in many areas including image
processing, medical diagnostics and autonomous driving. However, DNNs can exhibit …

There is a dramatically increasing interest in the quality assurance for DNN-based systems
in the software engineering community. An emerging hot topic in this direction is structural …

The reliability of software that has a Deep Neural Network (DNN) as a component is urgently
important today given the increasing number of critical applications being deployed with …

Deep neural networks (DNN) have been deployed in many software systems to assist in
various classification tasks. In company with the fantastic effectiveness in classification …

The growing use of deep neural networks in safety-critical applications makes it necessary
to carry out adequate testing to detect and correct any incorrect behavior for corner case …

Deep learning (DL) has achieved remarkable progress over the past decade and been
widely applied to many safety-critical applications. However, the robustness of DL systems …

Deep Neural Network (DNN) testing is one of the most widely-used ways to guarantee the
quality of DNNs. However, labeling test inputs to check the correctness of DNN prediction is …

Deep neural network (DNN) has become increasingly popular and DNN testing is very
critical to guarantee the correctness of DNN, ie, the accuracy of DNN in this work. However …