Variability models in decision-making domains: Study case: cerebrovascular accidents' etiology (2017)
Product lines' paradigm has proven to be of great utility in the context of decision making. Through the representation of product variance, and the selection of specific configurations the efficiency of this process has been greatly improved. Using methods like constraint programming as well as variability models, technology has supported a configuration process based on stakeholder's interests. Nevertheless, this approach's utility hasnþt been fully exploited as 1) it has been used, almost exclusively, in technologically-driven domains; and 2) performance issues have become a problem when dealing with large product lines and runtime environments. Therefore, this paper aims to elucidate how to extend the product's line paradigm impact. In the first place by illustrating its usage in a domain such as the medical one, and in the second place exploring an approach that can provide a response to the performance and scalability concerns. Thus, the employment of satisfiability modulo theories-based (SMT) algorithms is evaluated considering the evidence of their high efficiency and expressiveness