Category Theory Framework for Variability Models with Non-Functional Requirements
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Software Product Lines (SPLs) make use of Variability Models (VMs) as an input to automated reasoners, which are mainly used to generate optimal product configurations according to certain Quality Attributes (QAs). However, VMs and more specifically those including numerical features (i.e., NVMs), do not natively support QAs, and consequently, neither do automated reasoners commonly used in variability resolution. However, those satisfiability and optimisation problems have been covered and refined in other relational models such as databases.
Category Theory (CT) is an abstract mathematical theory typically used to capture the common aspects of seemingly dissimilar algebraic structures. We propose a unified relational modelling framework subsuming the structured objects of VMs and QAs and their relationships into algebraic categories. This abstraction allows a combination of automated reasoners over different domains to analyse SPLs. The solutions optimisation can now be natively performed by a combination of automated theorem proving, hashing, balanced-trees and chasing algorithms. We validate this approach by means of the edge computing SPL tool HADAS.