In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …

Abstract Context: A Machine Learning based System (MLS) is a software system including
one or more components that learn how to perform a task from a given data set. The …

This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

The past decade has seen the great potential of applying deep neural network (DNN) based
software to safety-critical scenarios, such as autonomous driving. Similar to traditional …

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) systems are rapidly being adopted in safety and security critical
domains, urgently calling for ways to test their correctness and robustness. Testing of DL …

In recent years, generative adversarial networks (GANs) and its variants have achieved
unprecedented success in image synthesis. They are widely adopted in synthesizing facial …

Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …