Ideally, a software development methodology should include both the ability to specify non-functional requirements and to analyze them starting early in the lifecycle; the goal is to verify whether the system under development would be able to meet such requirements. This chapter considers quantitative performance analysis of UML software models annotated with performance attributes according to the standard "UML Profile for Modeling and Analysis of Real-Time and Embedded Systems" (MARTE). The chapter describes a model transformation chain named PUMA (Performance by Unified Model Analysis) that enables the integration of performance analysis in a UML-based software development process, by automating the derivation of performance models from UML+MARTE software models, and by facilitating the interoperability of UML tools and performance tools. PUMA uses an intermediate model called "Core Scenario Model" (CSM) to bridge the gap between different kinds of software models accepted as input and different kinds of performance models generated as output. Transformation principles are described for transforming two kinds of UML behaviour representation (sequence and activity diagrams) into two kinds of performance models (Layered Queueing Networks and stochastic Petri nets). Next, PUMA extensions are described for two classes of software systems: service-oriented architecture (SOA) and software product lines (SPL).

Additional Metadata
Persistent URL dx.doi.org/10.1007/978-3-642-30982-3_7
Citation
Petriu, D, Alhaj, M. (Mohammad), & Tawhid, R. (Rasha). (2012). Software performance modeling. doi:10.1007/978-3-642-30982-3_7