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

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

Recent effort to test deep learning systems has produced an intuitive and compelling test
criterion called neuron coverage (NC), which resembles the notion of traditional code …

The past decade has seen the great potential of applying deep neural network (DNN) based
software to safety-critical scenarios, such as autonomous driving. Similar to traditional …

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 …

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 …

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 …