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 …

Deep neural networks (DNNs) are increasingly being adopted for sensing and control
functions in a variety of safety and mission-critical systems such as self-driving cars …

Deep neural networks (DNN) have achieved tremendous development in the past decade.
While many DNN-driven software applications have been deployed to solve various tasks …

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 …

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 …

There is a growing body of research on developing testing techniques for Deep Neural
Networks (DNNs). We distinguish two general modes of testing for DNNs: Offline testing …

It is important to test convolutional neural networks (CNNs) to identify defects (eg error-
inducing inputs) before deploying them in security-sensitive scenarios. Although existing …

With the increasing adoption of Deep Neural Network (DNN) models as integral parts of
software systems, efficient operational testing of DNNs is much in demand to ensure these …

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 …

Testing deep neural networks (DNNs) has garnered great interest in the recent years due to
their use in many applications. Black-box test adequacy measures are useful for guiding the …