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 network (DNN) testing approaches have grown fast in recent years to test the correctness and robustness of DNNs. In particular, DNN coverage criteria are frequently …
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 …
J Wang, H Qiu, Y Rong, H Ye, Q Li, Z Li… - Proceedings of the 31st …, 2022 - dl.acm.org
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 …
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 …
Z Wang, H You, J Chen, Y Zhang… - 2021 IEEE/ACM 43rd …, 2021 - ieeexplore.ieee.org
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 …