Hafiyyan Fadhlillah, Kevin Feichtinger, Kristof Meixner, Lisa Sonnleithner, Rick Rabiser, Alois Zoitl,
"Towards Multidisciplinary Delta-Oriented Variability Management in Cyber-Physical Production Systems, ACM, 2022."
: Proceedings of the 16th International Working Conference on Variability Modelling of Software-Intensive Systems, ACM, New York, USA, Seite(n) 13:1-13:10, 2-2022, ISBN: 978-1-4503-9604-2
Original Titel:
Towards Multidisciplinary Delta-Oriented Variability Management in Cyber-Physical Production Systems, ACM, 2022.
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 16th International Working Conference on Variability Modelling of Software-Intensive Systems
Original Kurzfassung:
Cyber-Physical Production Systems (CPPSs) are complex systems comprised of software and hardware interacting with each other and the environment. In industry, over time, a plethora of CPPSs are developed to satisfy varying customer requirements and changing technologies. Managing variability is challenging, especially in multidisciplinary environments like in CPPS engineering. For instance, when supporting the automatic derivation and configuration of control software, one needs to understand variability from not only a software perspective, but also a mechatronic, electrical, process, and business perspective. It is unrealistic to use a single model or even one type of model across these perspectives. In this paper, we describe a Multidisciplinary Delta-Oriented Variability Management approach for CPPSs that we are currently developing. Our approach aims to express CPPS variability in different disciplines using heterogeneous variability models, relating models via cross-discipline constraints, and automatically generating control software based on variability models. We implemented a prototype of our approach by realizing delta-oriented variability modeling for IEC 61499-based distributed control software and a configuration tool to enact the configuration options from multiple variability models. We performed a feasibility study of our approach using two systems of different size and complexity. We conclude that, despite current limitations, our approach can successfully and automatically generate control software based on related multidisciplinary variability models. We think that our approach is a good starting point to manage CPPS variability in practice.
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