In software-intensive systems, navigating the complexities that emerge from the interaction of design variability and stochastic
operational uncertainties presents a daunting challenge. This paper delves into the dynamics between these two dimensions
of uncertainty, offering novel insights about how modeling can contribute to the analysis of their combined impact upon
system properties. By elevating the abstraction level at which probabilistic models are conceptualized, our approach enables
an integrated analysis framework that considers both structural and quantitative dimensions of design spaces. Through the
introduction of novel language constructs, our methodology facilitates the direct referencing of structural relationships within
probabilistic behavioral specifications. Furthermore, the adoption of novel quantifiers in probabilistic temporal logic enables
evaluating complex properties across diverse design variants, thereby streamlining the assessment of guarantees within
the solution space. We demonstrate the feasibility of this approach on four case studies, showcasing its potential to offer
comprehensive insights into the trade-offs and decision-making processes inherent in managing different types of structural
design variability and operational uncertainties in software-intensive systems.
