Representing software usage models with stochastic automata networks

Abstract

The benefits of usage models in the statistical testing of software have been recognized by the software engineering community and discrete state formalisms have been largely used to describe such usage models of software. The Markov Chains formalism (MC) is a natural choice to do so. However, in this paper we suggest the use of a more sophisticated discrete state formalism, the Stochastic Automata Networks (SAN). In many problems of the performance evaluation area, the SAN models present advantages over MC models. Therefore, it seems natural to us to verify similar advantages in the modeling of usage models.This paper presents a case study of building a software usage model using SAN formalism. A software tool called DOCSEDITOR is modeled using MC and SAN. The models are compared in terms of number of states, scalability, and readability. It is not the objective of this paper to present a full framework to develop and analyse usage models with SAN, but just to show some evident advantages of the use of SAN instead of MC. In order to do this, the conclusion points out the indexes than can be computed from both models and suggests the next steps on future work.