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This is a pre-print, please access and cite the published version: https://doi.org/10.1145/3546932.3547007 The softwarisation and virtualisation of network functionality is the last milestone in the networking industry. Software-Defined Networks (SDN) and Network Function Virtualization (NFV) offer the possibility of using software to manage computer and mobile networks and build novel Virtual Network Functions (VNFs) deployed in heterogeneous devices. To reason about the variability of network functions and especially about the quality of a software product defined as a set of VNFs instantiated as part of a service (i.e., Service Function Chaining), a variability model along with a quality model is required. However, this domain imposes certain challenges to quality-aware reasoning of service function chains, such as numerical features or configuration-level Quality Attributes (QAs) (e.g., energy consumption). Incorporating numerical reasoning with quality data into SPL analyses is challenging and tool support is rare. In this work, we present 3 groups of operations: model report, aggregate functions to dynamically convert QAs at the feature-level into the configuration-level, and quality-aware optimisation. Our objective is to test the most complete reasoning tools to exploit the extended variability with quality attributes needed for VNFs., This 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., {"references":["Jose A Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez, and George Iosifidis. 2021. Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, IEEE, Paderborn, Germany, 1–2.","Albert Banchs, Marco Fiore, Andres Garcia-Saavedra, and Marco Gramaglia. 2021. Network intelligence in 6G: Challenges and opportunities. In Proceedings of the 16th ACMWorkshop on Mobility in the Evolving Internet Architecture. ACM, Nueva York, NY, USA, 7–12.","Michael Barr and Charles Wells. 1990. Category theory for computing science. Prentice Hall, Hoboken, New Jersey, USA.","David Benavides. 2019. Variability Modelling and Analysis During 30 Years. In From Software Engineering to Formal Methods and Tools, and Back - Essays Dedicated to Stefania Gnesi on the Occasion of Her 65th Birthday. Springer Berlin Heidelberg, Berlin, Heidelberg, 365–373. https://doi.org/10.1007/978-3-030- 30985-5_21","David Benavides, Sergio Segura, and Antonio Ruiz-Cortés. 2010. Automated analysis of feature models 20 years later: A literature review. Information Systems 35, 6 (2010), 615 – 636. https://doi.org/10.1016/j.is.2010.01.001","David Benavides, Pablo Trinidad, and Antonio Ruiz-Cortés. 2005. Automated Reasoning on Feature Models. In Advanced Information Systems Engineering, Oscar Pastor and João Falcão e Cunha (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 491–503.","Kristopher S Brown, David I Spivak, and Ryan Wisnesky. 2019. Categorical data integration for computational science. Computational Materials Science 164 (2019), 127–132.","Shubhajeet Chatterjee, Mohammad J Abdel-Rahman, and Allen B MacKenzie. 2021. On Optimal Orchestration of Virtualized Cellular Networks with Statistical Multiplexing. IEEE Transactions on Wireless Communications 21, 1 (2021), 310– 325.","Michel R. V. Chaudron. 2012. Quality Assurance in Model-Based Software Development: Challenges and Opportunities. In Software Quality. Process Automation in Software Development, Stefan Biffl, Dietmar Winkler, and Johannes Bergsmann (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 1–9.","João Choma Neto., Thelma E. Colanzi., and Aline M. M. Miotto Amaral. 2017. Application of Memetic Algorithms in the Search-based Product Line Architecture Design: An Exploratory Study. In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS,. INSTICC, SciTePress, Setubal, Portugal, 178–189. https://doi.org/10.5220/0006363201780189","Emanuel et al. Coutinho. 2018. Research Opportunities in Quality Assessment of Internet of Things, Software Defined Networks and Network Function Virtualization Environments. International Workshop on ADVANCEs in ICT Infrastructures and Services (ADVANCE 2018) 1 (2018), 43.","Xavier Devroey, Gilles Perrouin, Axel Legay, Pierre-Yves Schobbens, and Patrick Heymans. 2016. Search-Based Similarity-Driven Behavioural SPL Testing. In Proceedings of the Tenth International Workshop on Variability Modelling of Software- Intensive Systems (Salvador, Brazil) (VaMoS '16). Association for Computing Machinery, New York, NY, USA, 89–96. https://doi.org/10.1145/2866614.2866627","Sascha El-Sharkawy, Adam Krafczyk, and Klaus Schmid. 2019. MetricHaven: More than 23,000 Metrics for Measuring Quality Attributes of Software Product Lines. In Proceedings of the 23rd International Systems and Software Product Line Conference - Volume B (Paris, France) (SPLC '19). Association for Computing Machinery, New York, NY, USA, 25–28. https://doi.org/10.1145/3307630.3342384","Sascha El-Sharkawy, Adam Krafczyk, and Klaus Schmid. 2020. Fast Static Analyses of Software Product Lines: An Example with More than 42,000 Metrics. In Proceedings of the 14th International Working Conference on Variability Modelling of Software-Intensive Systems (Magdeburg, Germany) (VAMOS '20). Association for Computing Machinery, New York, NY, USA, Article 8, 9 pages. https://doi.org/10.1145/3377024.3377031","Sascha El-Sharkawy, Nozomi Yamagishi-Eichler, and Klaus Schmid. 2019. Metrics for analyzing variability and its implementation in software product lines: A systematic literature review. Information and Software Technology 106 (2019), 1–30. https://doi.org/10.1016/j.infsof.2018.08.015","Thiago do Nascimento Ferreira, Silvia Regina Vergilio, and Marouane Kessentini. 2020. Applying Many-Objective Algorithms to the Variability Test of Software Product Lines. In Proceedings of the 5th Brazilian Symposium on Systematic and Automated Software Testing (Natal, Brazil) (SAST 20). Association for Computing Machinery, New York, NY, USA, 11–20. https://doi.org/10.1145/3425174.3425211","Jaime Font, Lorena Arcega, Øystein Haugen, and Carlos Cetina. 2018. Achieving Feature Location in Families of Models Through the Use of Search-Based Software Engineering. IEEE Transactions on Evolutionary Computation 22, 3 (2018), 363–377. https://doi.org/10.1109/TEVC.2017.2751100","José A. Galindo and David Benavides. 2020. A Python Framework for the Automated Analysis of Feature Models: A First Step to Integrate Community Efforts. In Proceedings of the 24th ACM International Systems and Software Product Line Conference - Volume B (SPLC '20). ACM, New York, NY, USA, 52–55. https://doi.org/10.1145/3382026.3425773","Meihui Gao, Bernardetta Addis, Mathieu Bouet, and Stefano Secci. 2018. Optimal orchestration of virtual network functions. Computer Networks 142 (2018), 108– 127.","Gines Garcia-Aviles, Andres Garcia-Saavedra, Marco Gramaglia, Xavier Costa- Perez, Pablo Serrano, and Albert Banchs. 2021. Nuberu: Reliable RAN virtualization in shared platforms. In Proceedings of the 27th Annual International Conference on Mobile Computing and Networking. ACM, Nueva York, NY, USA, 749–761.","M. Gramaglia, L. Fuentes, M. Pinto, Daniel-Jesus Munoz, and et al. 2021. DAEMON Deliverable 3.1: Initial design of real-time control and VNF intelligence mechanisms. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No.101017109..","Jianmei Guo, Jia Hui Liang, Kai Shi, Dingyu Yang, Jingsong Zhang, Krzysztof Czarnecki, Vijay Ganesh, and Huiqun Yu. 2019. SMTIBEA: a hybrid multiobjective optimization algorithm for configuring large constrained software product lines. Software & Systems Modeling 18, 2 (2019), 1447–1466.","Christopher Henard, Mike Papadakis, Mark Harman, and Yves Le Traon. 2015. Combining Multi-Objective Search and Constraint Solving for Configuring Large Software Product Lines. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, Vol. 1. Association for Computing Machinery, New York, NY, USA, 517–528. https://doi.org/10.1109/ICSE.2015.69","Jose-Miguel Horcas, José A. Galindo, Ruben Heradio, David Fernandez-Amoros, and David Benavides. 2021. Monte Carlo Tree Search for Feature Model Analyses: A General Framework for Decision-Making. Association for Computing Machinery, New York, NY, USA, 190–201. https://doi.org/10.1145/3461001.3471146","Jose-Miguel Horcas, Mónica Pinto, and Lidia Fuentes. 2019. Software Product Line Engineering: A Practical Experience. In Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A (Paris, France) (SPLC '19). ACM, New York, New York, USA, 164–176. https://doi.org/10.1145/3336294. 3336304","ETSI ISG. 2022. Network Functions Virtualisation Standards. Technical Report. ETSI ISG NFV. https://docbox.etsi.org/ISG/NFV/Open/Publications_pdf/Specs- Reports/NFV-IFA%20011v4.3.1%20-%20GS%20-%20VNF%20Packaging%20Spec. pdf","Kyo C Kang, Sholom G Cohen, James A Hess, William E Novak, and A Spencer Peterson. 1990. Feature-oriented domain analysis (FODA) feasibility study. Technical Report. Carnegie-Mellon Univ Pittsburgh Pa Software Engineering Inst.","Alexander Knüppel, Thomas Thüm, Stephan Mennicke, Jens Meinicke, and Ina Schaefer. 2017. Is There a Mismatch between Real-World Feature Models and Product-Line Research?. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (Paderborn, Germany) (ESEC/FSE 2017). Association for Computing Machinery, New York, NY, USA, 291–302. https://doi.org/10.1145/3106237.3106252","Georg P Loczewski. 2018. A++ and the Lambda Calculus: Principles of Functional Programming. tredition, Berlin, Germany.","Roberto E. Lopez-Herrejon, Lukas Linsbauer, and Alexander Egyed. 2015. A systematic mapping study of search-based software engineering for software product lines. Information and Software Technology 61 (2015), 33–51. https: //doi.org/10.1016/j.infsof.2015.01.008","Lars Luthmann, Timo Gerecth, and Malte Lochau. 2019. Sampling strategies for product lines with unbounded parametric real-time constraints. International Journal on Software Tools for Technology Transfer 21 (2019), 613–633. https: //doi.org/10.1007/s10009-019-00532-4","T. Mahmoodi M. Condoluci. 2018. Softwarization and virtualization in 5G mobile networks: Benefits, trends and challenges. Computer Networks 146 (2018), 65–84.","Jens Meinicke, Thomas Thüm, Reimar Schröter, Fabian Benduhn, Thomas Leich, and Gunter Saake. 2017. Quality Assurance for Feature Models and Configurations. Springer International Publishing, Cham, 81–94. https://doi.org/10.1007/978-3- 319-61443-4_8","Seyedakbar Mostafavi, Vesal Hakami, and Maryam Sanaei. 2021. Quality of service provisioning in network function virtualization: a survey. Computing 103, 5 (2021), 917–991.","Daniel-Jesus Munoz, Dilian Gurov, Monica Pinto, and Lidia Fuentes. 2021. Category Theory Framework for Variability Models with Non-functional Requirements. In Advanced Information Systems Engineering, Marcello La Rosa, Shazia Sadiq, and Ernest Teniente (Eds.). Springer International Publishing, Cham, 397– 413.","Daniel-Jesus Munoz, Jeho Oh, Mónica Pinto, Lidia Fuentes, and Don Batory. 2019. Uniform Random Sampling Product Configurations of Feature Models That Have Numerical Features. In Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A (Paris, France). ACM, New York, New York, USA, 289–301. https://doi.org/10.1145/3336294.3336297","Daniel-Jesus Munoz, Mónica Pinto, and Lidia Fuentes. 2018. Finding correlations of features affecting energy consumption and performance of web servers using the HADAS eco-assistant. Computing 100, 11 (2018), 1155–1173.","Rafael Olaechea, Derek Rayside, Jianmei Guo, and Krzysztof Czarnecki. 2014. Comparison of Exact and Approximate Multi-Objective Optimization for Software Product Lines. In Proceedings of the 18th International Software Product Line Conference - Volume 1 (Florence, Italy) (SPLC '14). Association for Computing Machinery, NewYork, NY, USA, 92–101. https://doi.org/10.1145/2648511.2648521","Rafael Olaechea, Steven Stewart, Krzysztof Czarnecki, and Derek Rayside. 2012. Modelling and Multi-Objective Optimization of Quality Attributes in Variability-Rich Software. In Proceedings of the Fourth International Workshop on Nonfunctional System Properties in Domain Specific Modeling Languages (Innsbruck, Austria). ACM, New York, New York, USA, Article 2, 6 pages. https: //doi.org/10.1145/2420942.2420944","Gustavo G. Pascual, Roberto E. Lopez-Herrejon, Mónica Pinto, Lidia Fuentes, and Alexander Egyed. 2015. Applying multiobjective evolutionary algorithms to dynamic software product lines for reconfiguring mobile applications. J. Syst. Softw. 103 (2015), 392–411. https://doi.org/10.1016/j.jss.2014.12.041","Tobias Pett, Thomas Thüm, Tobias Runge, Sebastian Krieter, Malte Lochau, and Ina Schaefer. 2019. Product Sampling for Product Lines: The Scalability Challenge. In Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A (Paris, France) (SPLC '19). Association for Computing Machinery, New York, NY, USA, 78–83. https://doi.org/10.1145/3336294.3336322","Tie Qiu, Jiancheng Chi, Xiaobo Zhou, Zhaolong Ning, Mohammed Atiquzzaman, and Dapeng Oliver Wu. 2020. Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Communications Surveys & Tutorials 22, 4 (2020), 2462–2488.","Joan S Pujol Roig, David M Gutierrez-Estevez, and Deniz Gündüz. 2019. Management and orchestration of virtual network functions via deep reinforcement learning. IEEE Journal on Selected Areas in Communications 38, 2 (2019), 304–317.","Takfarinas Saber, David Brevet, Goetz Botterweck, and Anthony Ventresque. 2020. MILPIBEA: Algorithm for Multi-objective Features Selection in (Evolving) Software Product Lines, In Evolutionary Computation in Combinatorial Optimization - 20th European Conference, EvoCOP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15-17, 2020, Proceedings. Evolutionary Computation in Combinatorial Optimization - 20th European Conference, EvoCOP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15-17, 2020, Proceedings 12102, 164–179. https://doi.org/10.1007/978-3-030-43680-3_11","Anna Schmitt, Christian Bettinger, and Georg Rock. 2018. Glencoe–a tool for specification, visualization and formal analysis of product lines. In Transdisciplinary Engineering Methods for Social Innovation of Industry 4.0. IOS Press, Amsterdam, The Netherlands, 665–673.","Norbert Siegmund, Marko Rosenmüller, Martin Kuhlemann, Christian Kästner, Sven Apel, and Gunter Saake. 2012. SPL Conqueror: Toward Optimization of Non-Functional Properties in Software Product Lines. Software Quality Journal 20, 3–4 (sep 2012), 487–517. https://doi.org/10.1007/s11219-011-9152-9","Norbert Siegmund, Stefan Sobernig, and Sven Apel. 2017. Attributed Variability Models: Outside the Comfort Zone. In Proceedings of the 11th Joint Meeting on Foundations of Software Engineering (Paderborn, Germany) (ESEC/FSE 2017). ACM, New York, New York, USA, 268–278. https://doi.org/10.1145/3106237.3106251","David I Spivak and Robert E Kent. 2012. Ologs: a categorical framework for knowledge representation. PloS one 7, 1 (2012), e24274.","C.R. Systems. 2022. Sample Size Calc. https://www.surveysystem.com/sscalc.htm.","Paul Temple, Mathieu Acher, Jean-Marc Jézéquel, Léo Noel-Baron, and José Galindo. 2017. Learning-based performance specialization of configurable systems. Ph.D. Dissertation. IRISA, Inria Rennes; University of Rennes 1.","Pablo Trinidad. 2012. Automating the analysis of stateful feature models. Ph.D. Dissertation. Universidad de Sevilla. https://idus.us.es/handle/11441/55765","Pablo Trinidad, Antonio Ruiz-Cortés, and David Benavides. 2013. Automated Analysis of Stateful Feature Models. Springer Berlin Heidelberg, Berlin, Heidelberg, 375–380. https://doi.org/10.1007/978-3-642-36926-1_30","Mahsa Varshosaz, Mustafa Al-Hajjaji, Thomas Thüm, Tobias Runge, Mohammad Reza Mousavi, and Ina Schaefer. 2018. A Classification of Product Sampling for Software Product Lines. In Proceedings of the 22nd International Systems and Software Product Line Conference - Volume 1 (Gothenburg, Sweden) (SPLC '18). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3233027.3233035","Tobias Wägemann, Ramin Tavakoli Kolagari, and Klaus Schmid. 2019. ADOOPLA - Combining Product-Line- and Product-Level Criteria in Multi-objective Optimization of Product Line Architectures. In Software Architecture, Tomas Bures, Laurence Duchien, and Paola Inverardi (Eds.). Springer International Publishing, Cham, 126–142.","Lanxin Yang, He Zhang, Haifeng Shen, Xin Huang, Xin Zhou, Guoping Rong, and Dong Shao. 2021. Quality Assessment in Systematic Literature Reviews: A Software Engineering Perspective. Information and Software Technology 130 (2021), 106397. https://doi.org/10.1016/j.infsof.2020.106397","Anton Yrjönen and Janne Merilinna. 2009. Extending the NFR framework with measurable non-functional requirements. In Proceedings of the 2nd International Workshop on Non-functional System Properties in Domain Specific Modeling Languages, Marko Boškoviæ, Dragan Gaševiæ, Claus Pahl , and Bernhard Schätz (Eds.). ACM, New York, New York, USA, 0–14. 2nd International Workshop on Non-functional System Properties in Domain Specific Modeling Languages, NFPinDSML2009, NFPinDSML2009 ; Conference date: 04-10-2009 Through 04- 10-2009.","Guoheng Zhang, Huilin Ye, and Yuqing Lin. 2014. Quality attribute modeling and quality aware product configuration in software product lines. Softw. Qual. J. 22, 3 (2014), 365–401. https://doi.org/10.1007/s11219-013-9197-z"]}

This is the live presentation of a conference paper. Please, access and cite the published version: https://biblioteca.sistedes.es/articulo/composicion-categorica-de-analisis-automaticos-para-lineas-de-productos-extendidas/ Para las Líneas de Productos Software (LPS) se necesitan operaciones que nos permitan analizar dicho software y el reúso de sus características. Los razonadores son herramientas que automatizan estas operaciones. Desde la extensión de LPS con diversos tipos atributos de calidad, el tipo y número de operaciones de razonamiento ha crecido más rápido que el desarrollo de los respectivos razonadores. En consecuencia, las operaciones de an´alisis extendido son en el mejor caso parcialmente soportadas por los razonadores estado-del-arte. Para este desafío, podemos aplicar un enfoque de Teoría de Categorías (TC); el álgebra abstracta que capta los componentes comunes de estructuras aparentemente diferentes. Basándonos en la flexibilidad de sus razonamientos, proponemos una metodología donde las operaciones extendidas sean composiciones configurables de un conjunto de operaciones reusables independientes. Por tanto, buscamos definir e implementar un framework de razonamiento funcional de LPS extendidas basado en TC., Este trabajo está financiado por el programa de investigación e innovación H2020 de la Unión Europea bajo el acuerdo de subvención DAEMON 101017109, por los proyectos también co-financiados por fondos FEDER LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 y Rhea P18-FR-1081, y la ayuda PRE2019-087496 del Ministerio de Ciencia e Innovación., {"references":["Barr, M., Wells, C.: Category theory for computing science. Prentice Hall (1990)","Batory, D.: Feature models, grammars, and propositional formulas. In: Obbink, H., Pohl, K. (eds.) Software Product Lines. pp. 7–20. Springer, Berlin (2005)","Breiner, S., Breiner, S., Pollard, B., Subrahmanian, E.: Workshop on Applied Category Theory: Bridging Theory and Practice. US Department of Commerce, National Institute of Standards and Technology (2020)","Horcas, J.M., Pinto, M., Fuentes, L.: Software product line engineering: A practical experience. In: Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A. p. 164–176. SPLC '19, Association for Computing Machinery, New York, NY, USA (2019)","Käköla, T., Duenas, J.C.: Software product lines. Springer (2006)","Munoz, D.J.: Achieving energy efficiency using a software product line approach. In: Proceedings of the 21st SPLC. p. 131–138. SPLC '17, ACM, NY, USA (2017)","Munoz, D.J., Gurov, D., Pinto, M., Fuentes, L.: Category theory framework for variability models with non-functional requirements. In: Advanced Information Systems Engineering. pp. 397–413. Springer, Cham (2021)","SCHULTZ, P., WISNESKY, R.: Algebraic data integration. Journal of Functional Programming 27, e24 (2017)","Siegmund, N., Sobernig, S., Apel, S.: Attributed variability models: Outside the comfort zone. In: Proceedings of the 11th Joint Meeting on Foundations of Software Engineering. p. 268–278. ACM, New York, New York, USA (2017)","Trinidad, P., Ruiz-Cort´es, A., Benavides, D.: Automated Analysis of Stateful Feature Models, pp. 375–380. Springer Berlin Heidelberg, Berlin, Heidelberg (2013)"]}