Kevin Feichtinger, Rick Rabiser,
"Towards Transforming Variability Models: Usage Scenarios, Required Capabilities and Challenges"
, in ACM: SPLC '20: Proceedings of the 24th ACM International Systems and Software Product Line Conference - Volume B, Vol. B, ACM, New York, Seite(n) 44-51, 2020, ISBN: 978-1-4503-7570-2
Towards Transforming Variability Models: Usage Scenarios, Required Capabilities and Challenges
Sprache des Titels:
SPLC '20: Proceedings of the 24th ACM International Systems and Software Product Line Conference - Volume B
A plethora of variability modeling approaches has been developed in the last 30 years, e.g., feature modeling, decision modeling, Orthogonal Variability Modeling (OVM), and UML-based variability modeling. While feature modeling approaches are probably the most common and well-known group of variability modeling approaches, even within that group multiple variants have been developed, i.e., there is not just one type of feature model. Many variability modeling approaches have been demonstrated as useful for a certain purpose, e.g., domain analysis or configuration of products derived from a software product line. Nevertheless, industry frequently develops their own custom solutions to manage variability. The (still growing) number of modeling approaches simply makes it difficult to find, understand, and eventually pick an approach for a specific (set of) systems or context. In this paper, we discuss usage scenarios, required capabilities and challenges for an approach for (semi-)automatically transforming variability models. Such an approach would support researchers and practitioners experimenting with and comparing different variability models and switching from one modeling approach to another. We present the key components of our envisioned approach and conclude with a research agenda.