Architecture-driven reliability optimization with uncertain model parameters

It is currently considered good software engineering practice to decide between design alternatives based on quantitative architecture evaluations for different quality attributes, such as reliability and performance. However, the results of these quantitative architecture evaluations are dependent on design-time estimates for a series of model-parameters, which may not be accurate and have to be estimated subject heterogeneous uncertain factors. As a result, sub-optimal design decisions may be taken. To overcome this problem, we present a novel robust optimization approach that deals with parameter uncertainties at the design phase of software-intensive systems. This work specifically focuses on architecture-based reliability evaluation models. The proposed approach is able to find good solutions that restrict the impact of parameter uncertainties, and thus provides better decision support. The accuracy and scalability of the presented approach is validated with an industrial case study and a series of experiments with generated examples in different problem sizes