2026-06-03T05:41:09Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/409912026-02-03T12:18:44Zcom_10630_2254col_10630_37959

Nemo: A Tool to Transform Feature Models with Numerical Features and Arithmetic Constraints. Muñoz-Guerra, Daniel Jesús Oh, Jeho Pinto-Alarcón, Mónica Fuentes-Fernández, Lidia Batory, Don Ingeniería del software 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. Batory is retired, writing free textbooks [5], and is walking dogs for wages. https://www.springernature.com/gp/open-science/policies/book-policies Real-world Software Product Lines (SPLs) need Numerical Feature Models (NFMs) whose features not only have boolean values satisfying boolean constraints, but also have numeric attributes satisfying arithmetic constraints. A key operation on NFMs finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting. Nemo (Numbers, features, models) supports NFMs by bit-blasting, the technique that encodes arithmetic as boolean clauses. Nemo translates NFMs to propositional formulas whose products can be counted efficiently by #SAT solvers, enabling near-optimal products to be found. We evaluate Nemo with a diverse set of real-world NFMs, complex arithmetic constraints, and counting experiments in this paper. 2025-12-04T10:13:03Z 2025-12-04T10:13:03Z 2022-06-10 conference output 978-3-031-08129-3 1611-3349 https://hdl.handle.net/10630/40991 10.1007/978-3-031-08129-3_4 eng 20th International Conference on Software and Systems Reuse, ICSR 2022 Montpellier, Francia 15 a 17 Junio info:eu-repo/grantAgreement/EC/H2020/101017109/EU/Network intelligence for aDAptive and sElf-learning MObile Networks/DAEMON info:eu-repo/grantAgreement/Junta de Andalucia/FEDER/UMA18-FEDERJA-15/ES/LEIA/LEIA info:eu-repo/grantAgreement/Ministerio de Ciencia e Innovacion/Retos Investigacion/RTI2018-099213-B-I00/ES/Metodologías para el Desarrollo de Aplicaciones IoT de Extremo a Extremo/MEDEA info:eu-repo/grantAgreement/Junta de Andalucia/PAIDI 2020/P18-FR-1081/ES/Reingeniería para la Heterogeneidad y Evolución de Aplicaciones/Rhea info:eu-repo/grantAgreement/Ministerio de Ciencia e Innovacion/FPI/PRE2019-087496/ES/Ayudas para contratos predoctorales para la formación de doctores/PRE2019 open access Springer Nature