Generating a Performance Stochastic Model from UML Specifications
This work addresses software designers needing performance evaluation during design, but it is incremental as it builds on existing methods using UML to derive performance models.
The authors tackled the problem of predicting software performance early in the design phase by deriving a Stochastic Automata Network (SAN) from UML specifications, resulting in a more flexible approach due to SAN's modularity and similarity to UML state-chart diagrams.
Since its initiation by Connie Smith, the process of Software Performance Engineering (SPE) is becoming a growing concern. The idea is to bring performance evaluation into the software design process. This suitable methodology allows software designers to determine the performance of software during design. Several approaches have been proposed to provide such techniques. Some of them propose to derive from a UML (Unified Modeling Language) model a performance model such as Stochastic Petri Net (SPN) or Stochastic process Algebra (SPA) models. Our work belongs to the same category. We propose to derive from a UML model a Stochastic Automata Network (SAN) in order to obtain performance predictions. Our approach is more flexible due to the SAN modularity and its high resemblance to UML' state-chart diagram.