For organizations, the development of new business models and competitive advantages
through the integration of artificial intelligence (AI) in business and IT strategies holds …

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 …

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 introduction of machine learning (ML) components in software projects has created the
need for software engineers to collaborate with data scientists and other specialists. While …

The growing application of deep neural networks in safety-critical domains makes the
analysis of faults that occur in such systems of enormous importance. In this paper we …

The development of AI applications is a multidisciplinary effort, involving multiple roles
collaborating with the AI developers, an umbrella term we use to include data scientists and …

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

Context: In traditional software systems, Requirements Engineering (RE) activities are well-
established and researched. However, building Artificial Intelligence (AI) based software …

Background. The increasing reliance on applications with machine learning (ML)
components calls for mature engineering techniques that ensure these are built in a robust …

Companies struggle to continuously develop and deploy Artificial Intelligence (AI) models to
complex production systems due to AI characteristics while assuring quality. To ease the …