On the performance of UML state machine interpretation at runtime

  • Authors:

  • Edzard Höfig;Peter H. Deussen;Ina Schieferdecker

  • Affiliations:

  • Fraunhofer FOKUS, Berlin, Germany;Fraunhofer FOKUS, Berlin, Germany;Technical University of Berlin, Berlin, Germany

  • Venue:
  • Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

Modelling system behaviour by means of UML Behavioral State Machines is an established practice in software engineering. Usually, code generation is employed to create a system's software components. Although this approach yields software with a good runtime performance, the resulting system behaviour is static. Changes to the behaviour model necessarily provoke an iteration in the code generation workflow and a re-deployment of the generated artefacts. In the area of autonomic systems engineering, it is assumed that systems are able to adapt their runtime behaviour in response to a changing context. Thus, the constraints imposed by a code generation approach make runtime adaptation difficult, if not impossible. This article investigates a solution to this problem by employing interpretation techniques for the runtime execution of UML State Machines, enabling the adaptability of a system's runtime behaviour on the level of single model elements. This is done by devising concepts for behaviour model interpretation, which are then used in a proof-of-concept implementation to demonstrate the feasibility of the approach. For a quantitative evaluation we provide a performance comparison between the proof-of-concept implementation and generated code for a number of benchmark models. We find that UML State Machine interpretation has a performance overhead when compared with static code generation, but found it to be adequate for the majority of situations, except when dealing with high-throughput or delay-sensitive data.