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

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

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 …

The explorative and iterative nature of develo** and operating ML applications leads to a
variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software …

Context: Artificial intelligence (AI) components used in building software solutions have
substantially increased in recent years. However, many of these solutions focus on technical …

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 …

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 …

Artificial Intelligence (AI) has become a core feature of today's real-world applications,
making it a trending topic within the software engineering (SE) community. The rise in the …

Automata learning is a technique to infer an automaton model of a black-box system via
queries to the system. In recent years it has found widespread use both in industry and …

Driving automation systems, including autonomous driving and advanced driver assistance,
are an important safety-critical domain. Such systems often incorporate perception systems …