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Integrating Variability Modeling of Products, Processes, and Resources in Cyber-Physical Production Systems Engineering

Published: 27 October 2020 Publication History

Abstract

The Industry 4.0 initiative envisions the flexible and optimized production of customized products on Cyber-Physical Production Systems (CPPSs) that consist of subsystems coordinated to conduct complex production processes. Hence, accurate CPPS modeling requires integrating the modeling of variability for Product-Process-Resource (PPR) aspects. Yet, current variability modeling approaches treat structural and behavioral variability separately, leading to inaccurate CPPS production models that impede CPPS engineering and optimization. This paper proposes a PhD project for integrated variability modeling of PPR aspects to improve the accuracy of production models with variability for CPPS engineers and production optimizers. The research project follows the Design Science approach aiming for the iterative design and evaluation of (a) a framework to categorize currently incomplete and scattered models and methods for PPR variability modeling as a foundation for an integrated model; and (b) a modeling approach for more accurate integrated PPR variability modeling. The planned research will provide the Software Product Line (SPL) and CPPS engineering research communities with (a) novel models, methods, and insights on integrated PPR variability modeling, (b) open data from CPPS engineering use cases for common modeling, and (c) empirical data from field studies for shared analysis and evaluation.

References

[1]
Sofia Ananieva, Matthias Kowal, Thomas Thüm, and Ina Schaefer. 2016. Implicit constraints in partial feature models. In Proc. of the 7th Int. FOSD Workshop, FOSD@SPLASH 2016, Amsterdam, Netherlands, October 30, 2016. 18--27.
[2]
Sven Apel, Don Batory, Christian Kaestner, and Gunter Saake. 2013. Feature-Oriented Software Development: Concepts and Implementation. Springer.
[3]
Rabih Bashroush, Muhammad Garba, Rick Rabiser, Iris Groher, and Goetz Botterweck. 2017. CASE Tool Support for Variability Management in Software Product Lines. Comput. Surveys 50, 1 (2017), 14:1--14:45.
[4]
Luca Berardinelli, Alexandra Mazak, Oliver Alt, Manuel Wimmer, and Gerti Kappel. 2017. Model-driven systems engineering: Principles and application in the CPPS domain. In Multi-Disciplinary Engineering for Cyber-Physical Production Systems. Springer, 261--299.
[5]
Thorsten Berger, Ralf Rublack, Divya Nair, Joanne M Atlee, Martin Becker, Krzysztof Czarnecki, and Andrzej Wąsowski. 2013. A survey of variability modeling in industrial practice. In Proc. of the 7th Int. Workshop on Variability Modelling of Software-intensive Systems. ACM, 7--14.
[6]
Stefan Biffl, Detlef Gerhard, and Arndt Lüder. 2017. Introduction to the Multi-Disciplinary Engineering for Cyber-Physical Production Systems. In Multi-Disciplinary Engineering for Cyber-Physical Production Systems. Springer, 1--24.
[7]
Birte Caesar, Michael Nieke, Aljosha Köcher, Constantin Hildebrandt, Christoph Seidl, Alexander Fay, and Ina Schaefer. 2019. Context-sensitive reconfiguration of collaborative manufacturing systems. IFAC-PapersOnLine 52, 13 (2019), 307--312. 9th IFAC Conf. on Manufacturing Modelling, Management and Control.
[8]
Victor R Basili-Gianluigi Caldiera and H Dieter Rombach. 1994. Goal question metric paradigm. Encyclopedia of software engineering 1 (1994), 528--532.
[9]
Lianping Chen and M. Ali Babar. 2011. A systematic review of evaluation of variability management approaches in software product lines. IST 53, 4 (2011), 344--362.
[10]
Krzysztof Czarnecki, Paul Grünbacher, Rick Rabiser, Klaus Schmid, and Andrzej Wasowski. 2012. Cool Features and Tough Decisions: A Comparison of Variability Modeling Approaches. In Proc. of the 6th Int. Workshop on Variability Modelling of Software-intensive Systems. ACM, 173--182.
[11]
Patricia Derler, Edward A Lee, and Alberto Sangiovanni Vincentelli. 2012. Modeling cyber--physical systems. Proc. of the IEEE 100, 1 (2012), 13--28.
[12]
Matthias Galster, Danny Weyns, Dan Tofan, Bartosz Michalik, and Paris Avgeriou. 2014. Variability in Software Systems - A Systematic Literature Review. IEEE TSE 40, 3 (mar 2014), 282--306.
[13]
Volkan Gunes, Steffen Peter, Tony Givargis, and Frank Vahid. 2014. A survey on concepts, applications, and challenges in cyber-physical systems. KSII Transactions on Internet & Information Systems 8, 12 (2014).
[14]
Kurt W. Helbing. 2018. Typenvertreter. Springer Berlin Heidelberg, Berlin, Heidelberg, 1423--1428.
[15]
C. Hildebrandt, A. Scholz, A. Fay, T. Schröder, T. Hadlich, C. Diedrich, M. Dubovy, C. Eck, and R. Wiegand. 2017. Semantic modeling for collaboration and cooperation of systems in the production domain. In 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). 1--8.
[16]
Gerald Holl, Paul Grünbacher, and Rick Rabiser. 2012. A Systematic Review and an Expert Survey on Capabilities Supporting Multi Product Lines. IST 54, 8 (2012), 828--852.
[17]
IEC. 2013. IEC 61131-3:2013 - Programmable controllers - Part 3: Programming languages. https://webstore.iec.ch/publication/4552
[18]
IEC. 2013. IEC 62264-1:2013 - Enterprise-control system integration - Part 1: Models and terminology. https://webstore.iec.ch/publication/6675
[19]
Didac Gil De La Iglesia and Danny Weyns. 2015. MAPE-K Formal Templates to Rigorously Design Behaviors for Self-Adaptive Systems. ACM Trans. Auton. Adapt. Syst. 10, 3, Article 15 (Sept. 2015), 31 pages. https://doi.org/10.1145/2724719
[20]
Paulius Juodisius, Atrisha Sarkar, Raghava Rao Mukkamala, Michal Antkiewicz, Krzysztof Czarnecki, and Andrzej Wasowski. 2019. Clafer: Lightweight Modeling of Structure, Behaviour, and Variability. Art Sci. Eng. Program. 3, 1 (2019), 2.
[21]
Lukas Kathrein, Arndt Lüder, Kristof Meixner, Dietmar Winkler, and Stefan Biffl. 2019. Production-Aware Analysis of Multi-disciplinary Systems Engineering Processes. In Proc. of the 21st Int. Conf. on Enterprise Information Systems - Vol. 2: ICEIS. INSTICC, SciTePress, 48--60.
[22]
Barbara Kitchenham and Stuart Charters. 2007. Guidelines for performing systematic literature reviews in software engineering. (2007).
[23]
Matthias Kowal, Sofia Ananieva, Thomas Thüm, and Ina Schaefer. 2017. Supporting the Development of Interdisciplinary Product Lines in the Manufacturing Domain. IFAC-PapersOnLine 50, 1 (2017), 4336--4341.
[24]
Jacob Krüger, Sebastian Nielebock, Sebastian Krieter, Christian Diedrich, Thomas Leich, Gunter Saake, Sebastian Zug, and Frank Ortmeier. 2017. Beyond Software Product Lines: Variability Modeling in Cyber-Physical Systems. In Proc. of the 21st SPLC - Vol. A. ACM, New York, NY, USA, 237--241.
[25]
Anna-Lena Lamprecht, Stefan Naujokat, and Ina Schaefer. 2013. Variability Management beyond Feature Models. Computer 46, 11 (2013), 48--54.
[26]
Edward Lee. 2015. The past, present and future of cyber-physical systems: A focus on models. Sensors 15, 3 (2015), 4837--4869.
[27]
Kristof Meixner, Jakob Decker, Hannes Marcher, Arndt Lüder, and Stefan Biffl. 2020. Towards a Domain-Specific Language for Product-Process-Resource Constraints. In 2020 25th IEEE ETFA, Vienna, Austria, September 8-11 2020. IEEE.
[28]
Kristof Meixner, Lukas Kathrein, Arndt Lüder, Dietmar Winkler, and Stefan Biffl. 2020. Efficient Test Case Generation from Product and Process Model Properties and Preconditions. In 2020 25th IEEE ETFA, Vienna, Austria, Sep. 8-11 2020. IEEE.
[29]
Kristof Meixner, Arndt Lüder, Jan Herzog, Hannes Röpke, and Stefan Biffl. 2020. Modeling Expert Knowledge for Optimal CPPS Resource Selection for a Product Portfolio. In 2020 25th IEEE ETFA, Vienna, Austria, September 8-11 2020. IEEE.
[30]
Kristof Meixner, Rick Rabiser, and Stefan Biffl. 2019. Towards modeling variability of products, processes and resources in cyber-physical production systems engineering. In SPLC (B). ACM, 68:1--68:8.
[31]
Kristof Meixner, Rick Rabiser, and Stefan Biffl. 2020. Feature identification for engineering model variants in cyber-physical production systems engineering. In VaMoS. ACM, 18:1--18:5.
[32]
László Monostori. 2014. Cyber-physical Production Systems: Roots, Expectations and R&D Challenges. Procedia CIRP 17 (2014), 9--13.
[33]
Klaus Pohl, Günther Böckle, and Frank van der Linden. 2005. Software Product Line Engineering: Foundations, Principles, and Techniques. Springer.
[34]
Mikko Raatikainen, Juha Tiihonen, and Tomi Männistö. 2019. Software product lines and variability modeling: A tertiary study. JSS 149 (2019), 485--510.
[35]
Daniela Rabiser, Herbert Prähofer, Paul Grünbacher, Michael Petruzelka, Klaus Eder, Florian Angerer, Mario Kromoser, and Andreas Grimmer. 2018. Multipurpose, multi-level feature modeling of large-scale industrial software systems. Software and Systems Modeling 17, 3 (2018), 913--938.
[36]
Jan Oliver Ringert, Bernhard Rumpe, and Andreas Wortmann. 2015. Architecture and behavior modeling of cyber-physical systems with MontiArcAutomaton. arXiv preprint arXiv:1509.04505 (2015).
[37]
Dieter Rombach. 2005. Integrated software process and product lines. In Software Process Workshop. Springer, 83--90.
[38]
Marcello La Rosa, Wil M P Van Der Aalst, Marlon Dumas, and Fredrik P Milani. 2017. Business Process Variability Modeling: A Survey. Comput. Surveys 50, 1 (mar 2017), 2:1--2:45.
[39]
Emmanuelle Rouillé, Benoît Combemale, Olivier Barais, David Touzet, and Jean-Marc Jézéquel. 2012. Leveraging CVL to manage variability in software process lines. In Proc. 2012 19th Asia-Pacific Software Eng. Conf., Vol. 1. IEEE, 148--157.
[40]
Per Runeson, Martin Host, Austen Rainer, and Bjorn Regnell. 2012. Case study research in software engineering: Guidelines and examples. John Wiley & Sons.
[41]
Miriam Schleipen, Arndt Lüder, Olaf Sauer, Holger Flatt, and Jürgen Jasperneite. 2015. Requirements and concept for Plug-and-Work. at-Automatisierungstechnik 63, 10 (2015), 801--820.
[42]
Jocelyn Simmonds, Daniel Perovich, María Cecilia Bastarrica, and Luis Silvestre. 2015. A megamodel for software process line modeling and evolution. In Proc. of the 2015 ACM/IEEE 18th Int. Conf. on Model Driven Engineering Languages and Systems (MODELS). IEEE, 406--415.
[43]
Joshua Sprey, Chico Sundermann, Sebastian Krieter, Michael Nieke, Jacopo Mauro, Thomas Thüm, and Ina Schaefer. 2020. SMT-based variability analyses in FeatureIDE. In VaMoS. ACM, 6:1--6:9.
[44]
Frank van der Linden, Klaus Schmid, and Eelco Rommes. 2007. Software Product Lines in Action - The Best Industrial Practice in Product Line Engineering. Springer.
[45]
VDI/VDE 3682 2005. Formalised process descriptions. Beuth Verlag.
[46]
Birgit Vogel-Heuser and Stefan Biffl. 2016. Cross-discipline modeling and its contribution to automation. Automatisierungstechnik 64, 3 (2016), 165--167.
[47]
Roel Wieringa. 2014. Design science methodology for information systems and software engineering. Springer, Berlin [u.a.].
[48]
C Wohlin, P Runeson, M Höst, M Ohlsson, B Regnell, and A Wesslén. 2000. Introduction to Experimentation in Software Engineering.

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  • (2021)A Systematic Study as Foundation for a Variability Modeling Body of Knowledge2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA53835.2021.00012(25-28)Online publication date: Sep-2021

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    cover image ACM Conferences
    SPLC '20: Proceedings of the 24th ACM International Systems and Software Product Line Conference - Volume B
    October 2020
    139 pages
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    Published: 27 October 2020

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    Author Tags

    1. Cyber-Physical Production System
    2. Product-Process-Resource
    3. Variability Modelling

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    • (2021)A Systematic Study as Foundation for a Variability Modeling Body of Knowledge2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA53835.2021.00012(25-28)Online publication date: Sep-2021

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