Hafiyyan Fadhlillah,
"Multidisciplinary Variability Management for Cyber-Physical Production Systems. Proc. of the 26th ACM International Systems and Software Product Line Conference (SPLC 2022), Doctoral Symposium, Graz, Austria, ACM, 2022."
: Proceedings of the 26th ACM International Systems and Software Product Line Conference (SPLC 2022), Doctoral Symposium, ACM, New York, USA, Seite(n) 23-28, 2022, ISBN: 978-1-4503-9206-8
Original Titel:
Multidisciplinary Variability Management for Cyber-Physical Production Systems. Proc. of the 26th ACM International Systems and Software Product Line Conference (SPLC 2022), Doctoral Symposium, Graz, Austria, ACM, 2022.
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 26th ACM International Systems and Software Product Line Conference (SPLC 2022), Doctoral Symposium
Original Kurzfassung:
Cyber-Physical Production Systems (CPPSs) are complex, versatile systems interacting with the environment by sensors and actuators. Specific customer demands and technical requirements lead to high engineering efforts for the control software of CPPSs, especially when following a clone-and-own approach to reuse, as is still common in industry. Utilizing systematic variability management to derive and configure control software variants from a product line could help to reduce the cost of developing and/or maintaining CPPSs. However, modeling CPPS variability is challenging as knowledge from multiple disciplines (e.g., mechanics, electrics, software) is needed, which is either implicit in practice or expressed in multiple heterogeneous engineering artifacts with diverse semantics. Furthermore, techniques commonly used to implement CPPS control software (e.g., graphical programming or modeling languages) do not have any formal mechanism to express variability. In this paper, we report on our ongoing efforts to create a multidisciplinary variability management approach for CPPSs, particularly CPPS control software. We designed our approach as an integrated approach providing configuration options based on related heterogeneous variability models from multiple disciplines. Our integrated approach can generate control software based on related domain-specific implementation artifacts.
Forschungs-