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 …

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 …

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 …

Deep neural networks (DNNs) have a wide range of applications, and software employing
them must be thoroughly tested, especially in safety-critical domains. However, traditional …

Although current fuzz testing (fuzzing) methods are highly effective, there are still many
situations such as complex state machines where fully automated approaches fail. State-of …

Abstract Although Artificial Intelligence (AI) has become a buzzword for self-organizing IT
applications, its relevance to software engineering has hardly been analyzed systematically …

Deep neural networks (DNNs) are increasingly expanding their real-world applications
across domains, eg, image processing, speech recognition and natural language …

Natural language processing (NLP) models have been increasingly used in sensitive
application domains including credit scoring, insurance, and loan assessment. Hence, it is …

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 …

This survey presents an overview of verification techniques for autonomous systems, with a
focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents …