Rate Lifting for Stochastic Process Algebra by Transition Context Augmentation

Abstract

This paper presents an algorithm for determining the unknown rates in the sequential processes of a Stochastic Process Algebra (SPA) model, provided that the rates in the combined flat model are given. Such a rate lifting is useful for model reverse engineering and model repair. Technically, the algorithm works by solving systems of nonlinear equations and – if necessary – adjusting the model’s synchronisation structure, without changing its transition system. The adjustments cause an augmentation of a transition’s context and thus enable additional control over the transition rate. The complete pseudo-code of the rate lifting algorithm is included and discussed in the paper, and its practical usefulness is demonstrated by two case studies. The approach taken by the algorithm exploits some structural and behavioural properties of SPA systems, which are formulated here for the first time and could be very beneficial also in other contexts, such as compositional system verification.