Published July 1, 2021 | Version v1
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Category Theory Framework for Variability Models with Non-functional Requirements @ CAiSE 21

  • 1. Universidad de Málaga | ITIS Software
  • 2. KTH Royal Institute of Technology


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In Software Product Line (SPL) engineering one uses Variability Models (VMs) as input to automated reasoners to generate optimal products according to certain Quality Attributes (QAs). Variability models, however, and more specifically those including numerical features (i.e., NVMs), do not natively support QAs, and consequently, neither do automated reasoners commonly used for 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.


Munoz, Pinto and Fuentes work is supported by the European Union's H2020 research and innovation programme under grant agreement DAEMON 101017109, by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 and Rhea P18-FR-1081 and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación.


Daniel-Jesus Munoz CAiSE 21 Session 8 Theory Development and Use Category Theory Framework for Variability Models with Non-functional Requirements.mp4

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Related works

Is supplement to
Conference paper: 10.1007/978-3-030-79382-1_24 (DOI)
Presentation: 10.5281/zenodo.5062145 (DOI)


DAEMON – Network intelligence for aDAptive and sElf-Learning MObile Networks 101017109
European Commission