Introducing probabilities in Statecharts to specify reactive systems for performance analysis

Statecharts are expressed in a graphical language to specify complex reactive systems. They are extension of state-transition diagrams to which notions of hierarchy and orthogonality have been added. Recently, they have been suggested to represent performance models and in this regard a software package has been developed. In these performance models, the behavior of a system under study is considered to be probabilistic. Therefore, the inclusion of probabilities in Statecharts formalism will be studied. The proposed extension considers that a modeled system reacts probabilistically to events. In order to deal with these models, an analytical computational method based on constructing a Continuous-Time Markov Chain that is equivalent to the Statecharts model is proposed. The aspect of generating a Continuous-Time Markov Chain from Statecharts representation along with the solution to include probabilities among the transitions will be covered in this paper.Scope and purposeNowadays performance of a system can be analyzed before it is implemented based on the evaluation information. There are two approaches one can take into consideration in order to achieve performance evaluation of a given system. The first approach is simulation and the second one, which is the scope of this paper, is analytical solution. Analytical solutions are usually based on Markov chains from which steady-state probabilities, basis for performance measurements, can be determined. The main concern in obtaining these measurements is how to specify modern complex systems that react to events. A number of research efforts are being carried out in order to minimize the specification problem. The approach used in this paper proposes Statecharts, a specification method based on graphical representation, to specify a complex reactive system in a high-level fashion and then convert it into a Markov chain from which performance measurements can be obtained. This paper discusses the necessity of embedding probabilistic transitions among states in the sense that an event may probabilistically move to more than one different destinations. Examples included here explore Manufacturing systems but systems for other applications can also make use of this approach. It is expected that with this work it is possible to specify complex systems in a high-level of abstraction and an automated framework will do the job to obtain the performance information of a given system.