Analytic Evaluation on Petri Net by Using Markov Chain Theory to Achieve Optimized Models

The quality of an architectural design of a software system has a great influence on achieving non-functional requirements of a system. A regular software development project is often influenced by non-functional factors such as the customers' expectations about the performance and reliability of the software as well as the reduction of the underlying risk. The evaluation of non-functional parameters of a software system at the early stages of design and its development process are often considered as major factors in dealing with these issues. Because these evaluations can help us to choose the most proper model which is the securest and the most reliable. In this paper, a method is presented to obtain performance parameters from Generalized Stochastic Petri Net (GSPN) to be able to analyze the stochastic behaviour of the system. The embedded Continuous Time Markov Chain (CTMC) is derived from the GSPN and the Markov chain theory is used to obtain the performance parameters. We have designed a case tool to obtain some performance parameters that we discuss about them in this paper in addition to a case study.