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 …

Artificial Intelligence (AI) or Machine Learning (ML) systems have been widely adopted as
value propositions by companies in all industries in order to create or extend the services …

Adding an ability for a system to learn inherently adds uncertainty into the system. Given the
rising popularity of incorporating machine learning into systems, we wondered how the …

Background: The number of Machine Learning (ML) systems developed in the industry is
increasing rapidly. Since ML systems are different from traditional systems, these differences …

There is increasing interest in machine learning (ML) techniques and their applications in
recent years. Although there has been intensive support by frameworks and libraries for the …

Machine-learning (ML) techniques are becoming more prevalent. ML techniques rely on
mathematics and software engineering. Researchers and practitioners studying best …

Artificial intelligence enabled systems have been an inevitable part of everyday life.
However, efficient software engineering principles and processes need to be considered …

Machine learning (ML) has emerged as a powerful tool for enhancing various aspects of
software development, revolutionizing traditional practices and opening new avenues for …

Machine learning systems are both complex and unique. Complex because they consist of
many different components and involve many different stakeholders. Unique because …

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