Bencomo, NellyMichael, JudithWeske, Mathias2024-02-192024-02-192024978-3-88579-742-5https://dl.gi.de/handle/20.500.12116/43627There is growing uncertainty about the runtime environment of software systems. Therefore, how the system should behave under different contexts cannot be fully predicted at design time. It is considerations such as these that have led to the development of self-adaptive systems (SAS), which can dynamically and autonomously reconfigure their behavior to respond to hanging external conditions. The use of Machine Learning (ML) and AI has exacerbated the issues by adding more uncertainty sources. The scope of the talk is in the areas of Model-driven Engineering (MDE), Requirements Engineering (RE), software engineering (SE), and the development of techniques to quantify uncertainty to improve decision-making. The explicit reatment of uncertainty by the running system improves its judgment to make decisions supported by evaluating evidence found during runtime, possibly including the human-in-the-loop. The speaker will discuss how quantification of uncertainty can improve requirement elicitation (using simulations, for example). The talk will also cover different approaches to quantifying uncertainty, models@run.time and their role in Human-Machine Teaming.enText/Conference Paper10.18420/modellierung2024_0031617-5468