Architecture-based reliability evaluation under uncertainty

Abstract

The accuracy of architecture-based reliability evaluations depends on a number of parameters that need to be estimated, such as environmental factors or system usage. Researchers have tackled this problem by including uncertainties in architecture evaluation models and solving them analytically and with simulations. The usual assumption is that the input parameter distributions are normal, and that it is sufficient to report the attributes that describe the system in terms of the mean and variance of the attribute. In this work, we introduce a simulation-based approach that can accommodate a diverse set of parameter range distributions, self-regulate the number of architecture evaluations to the desired significance level and reports the desired percentiles of the values which ultimately characterise a specific quality attribute of the system. We include a case study which illustrates the flexibility of this approach using the evaluation of system reliability.