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 …

Incorporating machine learning (ML) components into software products raises new
software-engineering challenges and exacerbates existing ones. Many researchers have …

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 …

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 …

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 …

There is a widespread demand for Artificial Intelligence (AI) software, specifically Machine
Learning (ML). It is getting increasingly popular and being adopted in various applications …

Unique developmental and operational characteristics of machine learning (ML)
components as well as their inherent uncertainty demand robust engineering principles are …

Purpose Manufacturing small and medium-sized enterprises (SMEs) have already noticed
the tangible benefits offered by artificial intelligence (AI). Several approaches have been …

Deploying machine learning models to production is challenging, partially due to the
misalignment between software engineering and machine learning disciplines but also due …

We identify and describe episodes of sensemaking around challenges in modern Artificial-
Intelligence (AI)-based systems development that emerged in projects carried out by IBM …