Extensions and Scalability Experiments of a Generic Model-Driven Architecture for Variability Model Reasoning

Until recently, the state-of-the-art of Software Product Line (SPL) configuration and verification automation consisted of a collection of ad-hoc approaches tightly coupling a single input Variability Modeling Language (VML) with a single constraint solver.To remedy this situation, a novel generic model-driven architecture was then proposed that enables using a variety of VMLs and solvers.The key ideas of this proposal were (a) the use of a standard logical language (CLIF) as a pivot between VMLs and solvers, and (b) the use of a standard data exchange format (JSON) to explicilty and declaratively specify the abstract syntax and semantics of the VMLs to be used in an SPL engineering project and the automated reasoning task to be performed by the solvers.In this article, we overcome the limitations of this initial proposal in three key ways: (1) we add the ability to reason on textual or hybrid VMLs, rather than only on diagrammatic VMLs, enhancing the versatility of the architecture on the input side; (2) we enable the use of solvers from a third paradigm, enhancing the versatility of the architecture on the output side; and, (3) we present the results of scalability performance experiments of an implementation of this architecture.These results have been achieved without significantly altering the architecture, demonstrating its agnosticism with respect to specific VMLs and solvers.It also shows that it can underlie the implementation of practical variability reasoning tools that scale up to real sized variability model analysis and configuration needs.