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 …

Scenario-based approaches have been receiving a huge amount of attention in research
and engineering of automated driving systems. Due to the complexity and uncertainty of 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 …

This paper proposes AV-FUZZER, a testing framework, to find the safety violations of an
autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the …

Self-driving cars rely on software which needs to be thoroughly tested. Testing self-driving
car software in real traffic is not only expensive but also dangerous, and has already caused …

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 Networks (DNNs) are the core component of modern autonomous driving
systems. To date, it is still unrealistic that a DNN will generalize correctly to all driving …

With the recent advances of Deep Neural Networks (DNNs) in real-world applications, such
as Automated Driving Systems (ADS) for self-driving cars, ensuring the reliability and safety …

Complex systems such as autonomous cars are typically built as a composition of features
that are independent units of functionality. Features tend to interact and impact one another's …