• DocumentCode
    3677721
  • Title

    Runtime Model-Based Safety Analysis of Self-Organizing Systems with S#

  • Author

    Axel Habermaier;Benedikt Eberhardinger;Hella Seebach;Johannes Leupolz;Wolfgang Reif

  • Author_Institution
    Inst. for Software &
  • fYear
    2015
  • Firstpage
    128
  • Lastpage
    133
  • Abstract
    Self-organizing systems present a challenge for model-based safety analysis techniques: At design time, the potential system configurations are unknown, making it necessary to postpone the safety analyses to runtime. At runtime, however, model checking based safety analysis techniques are often too time-consuming because of the large state spaces that have to be analyzed. Based on the S# framework´s support for runtime model adaptation, we modularize runtime safety analyses by splitting them into two parts, modeling and analyzing the self-organizing and non-self-organizing parts separately. With some additional heuristics, the resulting state space reduction facilitates the use of model checking based safety analysis techniques to analyze the safety of self-organizing systems. We outline this approach on a self-organizing production cell, assessing the self-organization´s impact on the overall safety of the system.
  • Keywords
    "Analytical models","Adaptation models","Runtime","Hazards","Robot kinematics"
  • Publisher
    ieee
  • Conference_Titel
    Self-Adaptive and Self-Organizing Systems Workshops (SASOW), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/SASOW.2015.26
  • Filename
    7306569