Generating a Performance Stochastic Model from UML Specifications

TL;DR

This paper presents a method to derive a stochastic automata network from UML specifications to enable flexible performance prediction during software design.

Contribution

It introduces a novel approach to generate SAN models from UML diagrams, enhancing flexibility and resemblance to UML state charts.

Findings

SAN models provide accurate performance predictions.

The approach improves flexibility over existing methods.

It facilitates early performance evaluation during design.

Abstract

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.