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 …

Machine learning (ML) has become a core feature for today's real-world applications,
making it a trending topic for the software engineering community. Requirements …

Solid requirements engineering has increasingly been recognized as the key to improved,
on-time, and on-budget delivery of software and systems projects. New software tools are …

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 …

This book offers a practical approach to understanding, designing, and building sound
software based on solid principles. Using a unique Q&A format, this book addresses the …

Today, many industrial processes are undergoing digital transformation, which often
requires the integration of well-understood domain models and state-of-the-art machine …

Artificial intelligence and deep learning are becoming increasingly prevalent in
contemporary software solutions. Explainable artificial intelligence (XAI) tools attempt to …

Abstract Systems that rely on Machine Learning (ML systems) have differing demands on
quality—known as non-functional requirements (NFRs)—from traditional systems. NFRs for …

Large collections of data help evolve deep learning into the state-of-the-art in solving many
artificial intelligence problems. However, the requirements engineering (RE) community has …

Engineering successful machine learning (ML)-enabled systems poses various challenges
from both a theoretical and a practical side. Among those challenges are how to effectively …