Deep Neural Networks (DNNs) have been widely used in various domains, such as
computer vision and software engineering. Although many DNNs have been deployed to …

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 Network (DNN) testing is one of the common practices to guarantee the quality
of DNNs. However, DNN testing in general requires a significant amount of test inputs with …

Although deep learning (DL) software has been pervasive in various applications, the
brittleness of deep neural networks (DNN) hinders their deployment in many tasks …

Testing deep learning-based systems is crucial but challenging due to the required time and
labor for labeling collected raw data. To alleviate the labeling effort, multiple test selection …

Despite numerous applications of deep learning technologies on critical tasks in various
domains, advanced deep neural networks (DNNs) face persistent safety and security …

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

With the use of Deep Learning (DL) in safety-critical domains, the systematic testing of these
systems has become a critical issue for human life. Due to the data-driven nature of Deep …

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 safety and robustness of deep neural networks (DNNs) are currently of great concern.
Adequate testing is commonly an effective technique to ensure the software's …