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 …

Among the many software testing techniques available today, fuzzing has remained highly
popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of …

Connected and autonomous vehicles (CAVs) will form the backbone of future next-
generation intelligent transportation systems (ITS) providing travel comfort, road safety …

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 …

Nowadays, we are witnessing a wide adoption of Machine learning (ML) models in many
software systems. They are even being tested in safety-critical systems, thanks to recent …

An increasingly popular set of techniques adopted by software engineering (SE)
researchers to automate development tasks are those rooted in the concept of Deep …

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 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 …