Evaluation of non-functional properties of a design (such as performance, dependability, security, etc.) can be enabled by design annotations specific to the property to be evaluated. Performance properties, for instance, can be annotated on UML designs by using the "UML Profile for Schedulability, Performance and Time (SPT)". However the communication between the design description in UML and the tools used for non-functional properties evaluation requires support, particularly for performance where there are many alternative performance analysis tools that might be applied. This paper describes a tool architecture called PUMA, which provides a unified interface between different kinds of design information and different kinds of performance models, for example Markov models, stochastic Petri nets and process algebras, queues and layered queues.The paper concentrates on the creation of performance models. The unified interface of PUMA is centered on an intermediate model called Core Scenario Model (CSM), which is extracted from the annotated design model. Experience shows that CSM is also necessary for cleaning and auditing the design information, and providing default interpretations in case it is incomplete, before creating a performance model.
The Object Management Group (OMG) is in the process of defining a UML Profile for Schedulability, Performance and Time that will enable the construction of models for making quantitative predictions regarding these characteristics. The paper proposes a graph-grammar based method for transforming automatically a UML model annotated with performance information into a Layered Queueing Network (LQN) performance model. The input to our transformation algorithm is an XML file that contains the UML model in XML format according to the standard XMI interface. The output is the corresponding LQN model description file, which can be read directly by existing LQN solvers. The LQN model structure is generated from the high-level software architecture and from deployment diagrams indicating the allocation of software components to hardware devices. The LQN model parameters are obtained from detailed models of key performance scenarios, represented as UML interaction or activity diagrams.
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