Despite considerable research efforts on handling uncertainty in self-adaptive systems, a
comprehensive understanding of the precise nature of uncertainty is still lacking. This paper …

Large Language Models (LLMs), with their abilities in knowledge acquisition and reasoning,
can potentially enhance the various aspects of Self-adaptive Systems (SAS). Yet, the …

With the combination of software and physical devices into so-called cyber-physical systems
(CPSs), Digital Twins (DTs) have emerged to handle the resulting complexity and efficiently …

Abstract Digital Twins (DTs) are high-fidelity virtual models that behave-like, look-like and
connect-to a physical system. In this work, the physical systems are operations and …

Online reinforcement learning (RL) is increasingly used for realizing adaptive systems in the
presence of design time uncertainty because Online RL can leverage data only available at …

Engineering long-running computing systems that achieve their goals under ever-changing
conditions pose significant challenges. Self-adaptation has shown to be a viable approach …

In its quest for approaches to taming uncertainty in self-adaptive systems (SAS), the
research community has largely focused on solutions that adapt the SAS architecture or …

An Internet-of-Things (IoT) system typically comprises many small computing elements
(nodes) that are battery-powered and communicate over a wireless network. These …

A/B testing is a common approach used in industry to facilitate innovation through the
introduction of new features or the modification of existing software. Traditionally, A/B tests …

Adaptive Systems (ASs) are capable to monitor their behavior and make adjustments when
quality goals are not achieved through the MAPE-K, a widely recognized reference model …