The main challenges are discussed together with the lessons learned from past and
ongoing research along the development cycle of machine learning systems. This will be …

Software companies commonly develop and maintain variants of systems, with different
feature combinations for different customers. Thus, they must cope with variability in space …

Software companies encounter variability in space as variants of software systems need to
be produced for different customers. At the same time, companies need to handle evolution …

Cyber–physical systems are commonly highly configurable. Testing such systems is
particularly challenging because they comprise numerous heterogeneous components that …

Code is often reused to facilitate collaborative development, to create software variants, to
experiment with new ideas, or to develop new features in isolation. Social-coding platforms …

Dealing with highly configurable systems is generally very complex. Researchers and
practitioners have conceived hundreds of different analysis techniques to deal with different …

The main challenges along with lessons learned from ongoing research in the application of
machine learning systems in practice are discussed, taking into account aspects of …

Highly configurable software gives developers more flexibility to meet different customer
requirements and enables users to better tailor software to their needs. However, variability …

Testing is a fundamental verification activity to produce high-quality software. However,
testing is a costly and complex activity. The success of software testing depends on the …

Forking provides a flexible and low-cost strategy for developers to adapt an existing project
to new requirements, for instance, when addressing different market segments, hardware …