The uncertainty interaction problem in self-adaptive systems

Abstract

The problem of mitigating uncertainty in self-adaptation has driven much of the research proposed in the area of software engineering for self-adaptive systems in the last decade. Although many solutions have already been proposed, most of them tend to tackle specific types, sources, and dimensions of uncertainty (e.g., in goals, resources, adaptation functions) in isolation. A special concern are the aspects associated with uncertainty modeling in an integrated fashion. Different uncertainties are rarely independent and often compound, affecting the satisfaction of goals and other system properties in subtle and often unpredictable ways. Hence, there is still limited understanding about the specific ways in which uncertainties from various sources interact and ultimately affect the properties of self-adaptive, software-intensive systems. In this SoSym expert voice, we introduce the Uncertainty Interaction Problem as a way to better qualify the scope of the challenges with respect to representing different types of uncertainty while capturing their interaction in models employed to reason about self-adaptation. We contribute a characterization of the problem and discuss its relevance in the context of case studies taken from two representative application domains. We posit that the Uncertainty Interaction Problem should drive future research in software engineering for autonomous and self-adaptive systems, and therefore, contribute to evolving uncertainty modeling towards holistic approaches that would enable the construction of more resilient self-adaptive systems.

Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications

“…This vector indicates that Alternative 2, wind energy, is the best energy source for the problem. Finally, the third technique is used to choose the alternative energy source to be implemented in the mine by applying Correlations (11) and (21). These correlations permit one to construct the membership functions of the set of non-dominated alternatives for each criterion.…”

“…Therefore, considering the uncertainty of goals can be understood as the search for a solution that generates a compromise between the requirements of the attributes related to the decision-making problem. This is a considerable challenge because, in real-world problems, the uncertainty of goals cannot be effectively captured solely based on applying formal models, as decision-makers' knowledge, experience, and intuition are often the only sources of information for decision-making [18,21].…”

Análise das Respostas do ChatGPT em Relação ao Conteúdo de Programação para Iniciantes

“…Os LLMs, categoria ao qual o ChatGPT pertence, são modelos probabilísticos [Cámara et al 2023] de processamento de linguagem natural que podem gerar textos semelhantes aos gerados por humanos, além de também poder gerar códigos de programac ¸ão com sucesso [Li et al 2022]. Os textos são gerados a partir de tokens, que são previstos probabilisticamente baseados no contexto, sendo que os modelos do ChatGPT são treinados para fazer essa previsão de maneira otimizada [Lee 2023].…”

Targeting uncertainty in smart CPS by confidence-based logic

“…Nevertheless, there are a number of works that are closely related to our approach and/or also employ statistical methods for handling uncertainty and managing variability -a recurring requirement in software engineering on capturing context dynamicity and reflecting its effect on system behavior. A systematic literature review in this is provided in Cámara et al (2017), including runtime variability in self-adaptive systems. We compare our method to the state-of-the-art in self-adaptive systems, and for this purpose partially follow the classification in Mahdavi-Hezavehi et al (2017) for uncertainty sources options:…”

“…Based on Stich, the Rainbow framework (Cámara et al, 2017) relies on the MAPE-K model and captures different kinds of uncertainties, such as in sensing and effecting. The uncertainties are represented with different models, such as ranges or different kinds of distributions.…”