Abstract Context: Machine Learning (ML) has become widely adopted as a component in
many modern software applications. Due to the large volumes of data available …

This article focuses on the problem of optimizing the system utility of Machine Learning (ML)-
based systems in the presence of ML mispredictions. This is achieved via the use of self …

Machine learning (ML) applications face many new, hardly predictable aspects in their
production environments. Detecting new aspects in an ML production environment and …

Abstract [Context] Systems that incorporate Machine Learning (ML) models, often referred to
as ML-enabled systems, have become commonplace. However, empirical evidence on how …

Machine Learning (ML) models are now commonly used as components in systems. As any
other component, ML components can produce erroneous outputs that may penalize system …

Machine learning (ML) components are used heavily in many current software systems, but
develo** them responsibly in practice remains challenging.'Responsible ML'refers to …

As machine learning (ML) components become in-creasingly integrated into software
systems, the emphasis on the ethical or responsible aspects of their use has grown …

Intent-based networking (IBN) systems have become the de-facto control abstraction to drive
self-service, self-healing, and self-optimized capabilities in service delivery processes …

[Context] Transfer learning techniques are commonly employed to address the problem of
distribution shifts and concept drifts in learning-enabled autonomic systems. Reusing …

Abstract The Internet of Things (IoT) technologies are often seen as being the main drivers of
the current technological revolution, which devotes the most priority to improving the well …