An efficient method for architecture-based reliability evaluation for evolving systems with changing parameters

Probabilistic models are widely used in Architecture-based reliability prediction in software intensive systems. However, for most of the cases, it is computationally expensive to compute the reliability metrics and re-compute them once the system has evolved or is used in a different environment. In this paper, we introduce an efficient computation method for Discrete Time Markov Chain based abstractions, which computes reliability metrics once, and we provide an incremental technique to recompute these metrics in case of a single change in the reliability evaluation model. As a result, fast and efficient reliability computation can be provided for scenarios like design-time architecture optimization and run time adaptation. An experimental validation of the new method shows a significant improvement in terms of computation time required to re-evaluate an evolved architecture