Abstract
Software Performance Engineering (SPE) has recently considered as an important issue in the software development process. It consists on evaluate the performance of a system during the design phase. We have recently proposed a new methodology to generate a Stochastic Automata Network (SAN) model from a UML model, to consequently obtain performance predications from UML specifications. In this paper, we expand our idea to cover more complex UML models taking in advantage the modularity of SAN in modeling large systems. A formal description of the generation process is presented. The new extension gives rise to a serious approach in SPE that we call UML2SAN.
Highlights
Software engineering has traditionally focused on functional requirements and how to build software that has few bugs and can be maintained
Affecting method should be predictable in the Unified Modeling Language (UML) model because they will drive the transitions in the Stochastic Automata Network (SAN) automata related to affecting variables, as it will be described in step 3
This paper is the continuity of our work that proposes a new methodology in the performance software engineering
Summary
Software engineering has traditionally focused on functional requirements and how to build software that has few bugs and can be maintained. .an important methodology, initially introduced by Smith [11], starts today to cover a large place in the software engineering area. This methodology, called Performance Software Engineering (SPE), consists essentially on introducing some techniques allowing obtaining performance predictions of the system basing on the design model. Our recent work in [10], initiates a preliminary methodology to derive from UML a Stochastic Automata Network (SAN) model, a Markov-based model used to generate performance predictions. We show the essential UML diagrams need for the derivation of the SAN model.
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