Nowadays, we are witnessing an increasing demand in both corporates and academia for
exploiting Deep Learning (DL) to solve complex real-world problems. A DL program …

As Deep Learning (DL) systems are widely deployed for mission-critical applications,
debugging such systems becomes essential. Most existing works identify and repair …

A growing demand is witnessed in both industry and academia for employing Deep
Learning (DL) in various domains to solve real-world problems. Deep reinforcement …

Deep Neural Networks (DNNs) are used in a wide variety of applications. However, as in
any software application, DNN-based apps are afflicted with bugs. Previous work observed …

The rapid and widespread adoption of Deep Neural Networks (DNNs) has called for ways to
test their behaviour, and many testing approaches have successfully revealed misbehaviour …

Deep learning has made significant achievements in many application areas. To train and
test models more efficiently, enterprise developers submit and run their deep learning …

At present, we are witnessing an increasing effort to improve the performance and
trustworthiness of Deep Neural Networks (DNNs), with the aim to enable their adoption in …

Training deep neural networks can generate non-descriptive error messages or produce
unusual output without any explicit errors at all. While experts rely on tacit knowledge to …

Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software
applications. These software applications, named as DL based software (in short as 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 …