Towards Model Quality Assurance for Multi-Disciplinary Engineering

Needs, Challenges and Solution Concept in an AutomationML Context
  • Dietmar Winkler
  • Manuel Wimmer
  • Luca Berardinelli
  • Stefan Biffl
Chapter

Abstract

In multi-disciplinary engineering (MDE) projects, information models play an important role as inputs to and outputs of engineering processes. In MDE projects, engineers collaborate from various disciplines, such as mechanical, electrical, and software engineering. These disciplines use general-purpose and domain-specific models in their engineering context. Important challenges include model synchronization and model quality assurance (MQA) that are covered insufficiently in current MDE practices. This chapter focuses on the needs and approaches for MQA in MDE environments. We address the following two research questions (RQs): The first RQ focuses on investigating needs and expected capabilities that are required for a systematic review process that focuses on changes in MDE design models (RQ-MQA1). The second RQ focuses on how to extend a standard modeling language for MDE, such as the AutomationML, to address needs for storing process-relevant attributes in the context of quality assurance and review process support (RQ-MQA2). This chapter presents concepts and an initial evaluation of MQA approaches in the context of selected MDE processes, i.e., the addition, change, or removal of a component in an engineering discipline and an impact analysis on the integrated plant model. Main results are that (a) an adapted review process helps to systematically drive model reviews for MDE and (b) the standardized language description of AutomationML can be extended with process-related attributes that are useful for quality assurance and reviewing.

Keywords

Model Quality Assurance Multi-Disciplinary Engineering Model Review Defect Detection AutomationML 

References

  1. Aurum, A., Petersson, H., Wohlin, C.: State-of-the-art: software inspections after 25 years. Softw. Test. Verification. Reliab. 12(3), 133–154 (2002)CrossRefGoogle Scholar
  2. Berardinelli, L., Biffl, S., Mätzler, E., Mayerhofer, T., Wimmer, M.: Model-based co-evolution of production systems and their libraries with AutomationML. In: Proceedings of the 20th IEEE Conference on Emerging Technologies & Factory Automation (ETFA). (2015)Google Scholar
  3. Biffl, S., Lüder, A., Winkler, D.: Multi-disciplinary engineering for Industrie 4.0—semantic challenges, needs, and capabilities. In: Biffl, S., Sabou, M. (eds.) Semantic web technologies for intelligent engineering applications. Springer, Switzerland (2016a)CrossRefGoogle Scholar
  4. Biffl, S., Mordinyi, R., Steininger, H., Winkler, D.: Integrationsplattform für anlagenmodellorientiertes Engineering—Bedarfe und Lösungsansätze. In: Vogel-Heuser, B., Bauernhansl, T., ten Hompel, M. (eds.) Handbuch Industrie 4.0, p. 2. Springer, Auflage (2016b)Google Scholar
  5. Blackwell, A.F., Britton, C., Cox, A., Green, T.R.G., Gurr, C., Kadoda, G., Kutar, M.S., Loomes, M., Nehaniv C.L., Petre, M., Roast, C., Wong, A., Young R.M.: Cognitive dimensions of notations: design tools for cognitive technology. In: Cognitive technology: instruments of mind, pp. 325–341. Springer, Berlin (2001)Google Scholar
  6. Brambilla, M., Cabot, J., Wimmer, M.: Model-driven software engineering in practice. Morgan & Claypool Publishers, California (2012)Google Scholar
  7. Drath, R. (ed.): Datenaustausch in der Anlagenplanung mit AutomationML: Integration von CAEX, PLCOpen XML und COLLADA. Springer, Berlin (2009)Google Scholar
  8. Feldmann, S., Herzig, S.J.I., Kernschmidt, K., Wolfenstetter, T., Kammerl, D., Qamar, A., Lindemann, U., Krcmar, H., Paredis, C.J.J., Vogel-Heuser, B.: Towards effective management of inconsistencies in model-based engineering of automated production systems. In: Proceedings of 15th IFAC Symposium on Information Control in Manufacturing (INCOM), 48(3), pp. 916–923 (2015)Google Scholar
  9. Göring, M., Fay, A.: Modeling change and structural dependencies of automation systems. In: Proceedings of 17th International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE (2012)Google Scholar
  10. IEC 62714-1: Engineering data exchange format for use in industrial automation systems engineering—Automation Markup Language—Part 1: Architecture and general requirement, International Standard, June 2014 (2014)Google Scholar
  11. IEC 62714-2: Engineering data exchange format for use in industrial automation systems engineering—Automation Markup Language—Part 2: Role class libraries, International Standard, March 2015 (2015)Google Scholar
  12. IEC 62714-3: Engineering data exchange format for use in industrial automation systems engineering—Automation Markup Language—Part 3: Geometry and Kinematics, International Standard, June 2017 (2017)Google Scholar
  13. ISO/PAS 17506: Industrial automation systems and integration: COLLADA digital asset schema specification for 3D visualization of industrial data, International Standard (2012)Google Scholar
  14. Kollanus, S., Koskinen, J.: Survey of Software Inspection Research: 1991-2005. Working papers WP-40, University of Jyväskylä (2007)Google Scholar
  15. Laitenberger, O., DeBaud, J.-M.: An encompassing life cycle centric survey of software inspection. J. Syst. Softw. 50(1), 5–31 (2000)CrossRefGoogle Scholar
  16. Lee, E.A.: Cyber physical systems: design and challenges, In: Proceedings of the 11th IEEE International Symposium on Project and Component-Oriented Real-Time Distributed Computing (ISORC), pp. 363–369 (2008)Google Scholar
  17. Milanesio, L.: Learning Gerrit code review, 144 p. Packt Publishing (2013)Google Scholar
  18. Mordinyi, R., Wimmer, M., Biffl, S.: Engineering model exchange in multi-disciplinary engineering with the AutomationML Hub. In: Proceedings of the 4th AutomationML User Conference, Esslingen, Germany (2016a)Google Scholar
  19. Mordinyi, R., Winkler, D., Ekaputra, F.J., Wimmer, M., Biffl, S.: Investigating model slicing capabilities on integrated plant models with AutomationML. In: Proceedings of the 21st IEEE Conference on Emerging Technologies & Factory Automation (ETFA), IEEE (2016b)Google Scholar
  20. Pohl, K., Böckle, G., van der Linden, F.J.: Software product line engineering: foundations, Principles and techniques. Springer, Berlin (2005)CrossRefMATHGoogle Scholar
  21. Schleipen, M., Drath, R., Sauer, O.: The system-independent data exchange format CAEX for supporting an automatic configuration of a production monitoring and control system, In: Proceedings of ISIE, pp. 1786–1791 (2008)Google Scholar
  22. Travassos, G., Shull, F., Fredericks, M., Basili, V.R.: Detecting defects in object-oriented designs—using reading techniques to increase software quality. In: ACM Sigplan, 34(10), pp. 47–56, ACM (1999)Google Scholar
  23. Vogel-Heuser, B., Bauernhansl, T., ten Tompel, M. (eds.): Handbuch Industrie 4.0. Springer, Berlin (2016)Google Scholar
  24. Whittle, J., Hutchinson, J., Rouncefield, M.: Model-driven development: a practical approach. Chapman & Hall/CRC, London, UK (2016)Google Scholar
  25. Winkler, D., Biffl, S.: Focused inspections to support defect detection in multi-disciplinary engineering environments. In: Proceedings of the 16th International Conference on Product-Focused Software Process Improvement (PROFES), Research Preview. Springer (2015)Google Scholar
  26. Winkler, D., Biffl, S., Steiniger, H.: Integration von heterogenen Engineering-Daten mit AutomationML und dem AML.hub: Konsistente Daten über Fachbereichsgrenzen hinweg. In: develop3, 3/2015 (2015)Google Scholar
  27. Winkler, D., Ekaputra, F.J., and Biffl, S.: AutomationML Review Support in Multi-Disciplinary Engineering Environments. In: Proceedings of ETFA, IEEE (2016a)Google Scholar
  28. Winkler, D., Mordinyi, R., Biffl, S.: Qualitätssicherung in heterogenen und verteilten Entwicklungsumgebungen für industrielle Produktionssysteme. In: Vogel-Heuser, B., Bauernhansl, T., ten Hompel, M. (eds.) Handbuch Industrie 4.0, p. 2. Springer, Auflage (2016b)Google Scholar
  29. Winkler, D., Sabou, M., Biffl, S.: Improving quality assurance in multi-disciplinary engineering environments with semantic technologies. In: Kounis, L.D. (ed.) Quality control and assurance. INTEC Publishing (2017)Google Scholar
  30. Xiong, Y., Liu, D., Hu, Z., Zhao, H., Takeichi, M., Hong, M.: Towards automatic model synchronization from model transformations, In: Proceedings of the 22nd International Conference on Automated Software Engineering (ASE), pp. 164–173. ACM (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dietmar Winkler
    • 1
    • 2
  • Manuel Wimmer
    • 2
  • Luca Berardinelli
    • 2
  • Stefan Biffl
    • 3
  1. 1.SBA-Research gGmbHViennaAustria
  2. 2.Technische Universität Wien, Institute of Software Technology and Interactive Systems, CDL-FlexWienAustria
  3. 3.Technische Universität WienWienAustria

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