Quantum software plays a critical role in exploiting the full potential of quantum computing
systems. As a result, it has been drawing increasing attention recently. This paper defines …

Deep learning (DL) techniques are rapidly developed and have been widely adopted in
practice. However, similar to traditional software systems, DL systems also contain bugs …

Deep learning (DL) systems can make our life much easier, and thus are gaining more and
more attention from both academia and industry. Meanwhile, bugs in DL systems can be …

Deep learning (DL) systems are widely used in domains including aircraft collision
avoidance systems, Alzheimer's disease diagnosis, and autonomous driving cars. Despite …

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 …

Input constraints are useful for many software development tasks. For example, input
constraints of a function enable the generation of valid inputs, ie, inputs that follow these …

Deep learning (DL) techniques are proven effective in many challenging tasks, and become
widely-adopted in practice. However, previous work has shown that DL libraries, the basis of …

Recent advances in deep neural networks (DNNs) have led to object detectors (ODs) that
can rapidly process pictures or videos, and recognize the objects that they contain. Despite …

The prosperous trend of deploying deep neural network (DNN) models to diverse hardware
platforms has boosted the development of deep learning (DL) compilers. DL compilers take …

Probabilistic programming systems and machine learning frameworks like Pyro, PyMC3,
TensorFlow, and PyTorch provide scalable and efficient primitives for inference and training …