References of "Istoan, Paul"
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See detailComparing Six Modeling Approaches
Mussbacher, Gunter; Al Abed, Wisam; Alam, Omar et al

in Kienzle, Joerg (Ed.) Models in Software Engineering (2012)

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See detailA Metamodel-based Classification of Variability Modeling Approaches
Istoan, Paul; Klein, Jacques UL; Perrouin, Gilles UL et al

in VARY, International Workshop affiliated with ACM/IEEE 14th International Conference on Driven Engineering Languages and Systems (2011)

Software Product Line Engineering (SPLE) is an emerging paradigm taking momentum that proposes to address flexibility and shorter time-to-market by maximizing software reuse. The key characteristic of ... [more ▼]

Software Product Line Engineering (SPLE) is an emerging paradigm taking momentum that proposes to address flexibility and shorter time-to-market by maximizing software reuse. The key characteristic of SPLE is the effective modelling and management of variability, for which a number of Variability Modeling (VM) techniques have been developed during the last two decades. Therefore, understanding their commonalities and differences is important for selecting the most suitable technique. In this paper, we propose a metamodel-based classification of VM techniques gathered through a survey of relevant literature. [less ▲]

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See detailIssues in model-driven behavioural product derivation
Istoan, Paul; Biri, Nicolas; Klein, Jacques UL

in Fifth International Workshop on Variability Modelling of Software-intensive Systems (Vamos 2011) (2011)

Model Driven Engineering (MDE) was identified as a viable software development paradigm to help improve the product derivation phase of the Software Product Line (SPL) engineering process. Existing model ... [more ▼]

Model Driven Engineering (MDE) was identified as a viable software development paradigm to help improve the product derivation phase of the Software Product Line (SPL) engineering process. Existing model-driven derivation approaches fail to properly address the behavioural derivation part, yielding a frustrating situation. In this paper we first introduce a modeldriven derivation approach that combines Feature Diagrams (FD) and model fragments. We then identify and analyse several issues that emerge during the derivation process. We show that the order in which models associated to selected features are composed has a great impact on the end result of the derivation. We also present a particular class of features called disjoint and prove that current composition operators do not offer any viable solution to compose them. Finally, we argue that insufficient information available to composition operators leads to derivation results that do not satisfy user requirements. [less ▲]

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