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 …

Background: Developing and maintaining large scale machine learning (ML) based
software systems in an industrial setting is challenging. There are no well-established …

The number and importance of AI-based systems in all domains is growing. With the
pervasive use and the dependence on AI-based systems, the quality of these systems …

Data validation is an essential requirement to ensure the reliability and quality of Machine
Learning-based Software Systems. However, an exhaustive validation of all data fed to …

Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

The recent active development of Internet of Things (IoT) solutions in various domains has
led to an increased demand for security, safety, and reliability of these systems. Security and …

AI solutions seem to appear in any and all application domains. As AI becomes more
pervasive, the importance of quality assurance increases. Unfortunately, there is no …

There is a growing interest in industry and academia in machine learning (ML) testing. We
believe that industry and academia need to learn together to produce rigorous and relevant …

The value stream method, a key tool in industry to analyze and visualize value streams in
production, aims to holistically optimize process steps, reduce waste, and achieve …

Abstract Context: The Data Stream Processing (DSP) approach focuses on real-time data
processing by applying specific techniques for capturing and processing relevant data for on …