Uncertainty-aware Design Decisions through Probabilistic Uncertainty Quantification and Consistent Merging

When developing complex, software-intensive or cyber-physical systems, uncertainty must be managed during all stages. Especially if made explicit, it can significantly affect the design decisions of various stakeholders.
A necessary requirement for determining the effect of uncertainty on a system and deciding whether it must be resolved is explicit quantification of uncertainty.
However, that alone is not enough. Often, in multi-model development processes, dependent parameters are affected by such quantified uncertainty.
To ensure efficient development and uncertainty-aware decision making, we present an approach that allows to quantify uncertainty probabilistically or through a sample set.
This quantified uncertainty is then kept consistent across all models.
Furthermore, uncertainty is propagated and merged onto downstream model elements that depend on model elements or parameters with quantified uncertainty, and the contribution of each uncertain input to the derived uncertainty is quantified using Sobol sensitivity indices.
We evaluate the approach using two case studies, comprising seven scenarios from cyber-physical systems and software engineering, based on industry scenarios and literature.
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The results show that quantified uncertainty can be expressed and correctly propagated and merged across all scenarios, matching an independent analytical oracle.