Deep learning (DL) has achieved remarkable progress over the past decade and has been
widely applied to many industry domains. However, the robustness of DL systems recently …

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 learning (DL) defines a new data-driven programming paradigm where the internal
system logic is largely shaped by the training data. The standard way of evaluating DL …

Deep learning (DL) has been applied widely, and the quality of DL system becomes crucial,
especially for safety-critical applications. Existing work mainly focuses on the quality …

Deep learning (DL) defines a new data-driven programming paradigm that constructs the
internal system logic of a crafted neuron network through a set of training data. We have …

Deep Learning (DL) components are routinely integrated into software systems that need to
perform complex tasks such as image or natural language processing. The adequacy of the …

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 increasing adoption of Deep Learning (DL) for critical tasks, such as autonomous
driving, the evaluation of the quality of systems that rely on DL has become crucial. Once …

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 …

As Deep Learning (DL) is continuously adopted in many industrial applications, its quality
and reliability start to raise concerns. Similar to the traditional software development …