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

Generative Artificial Intelligence (GenAI) tools have become increasingly prevalent in
software development, offering assistance to various managerial and technical project …

The rapid growth of applying Machine Learning (ML) in different domains, especially in
safety-critical areas, increases the need for reliable ML components, ie, a software …

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 …

In industry, RESTful APIs are widely used to build modern Cloud Applications. Testing them
is challenging, because not only do they rely on network communications, but also they deal …

Abstract Context: Engineering Artificial Intelligence (AI) software is a relatively new area with
many challenges, unknowns, and limited proven best practices. Big companies such as …

Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-
critical systems in which Machine Learning (ML) algorithms learn optimized and safe …

Machine Learning (ML) is an application of Artificial Intelligence (AI) that uses big data to
produce complex predictions and decision-making systems, which would be challenging to …

Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …

Abstract Adversarial Machine Learning (AML) discusses the act of attacking and defending
Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML …