Improving Variabilty Analysis through Scenario-Based Incompatibility Detection

Autores
Buccella, Agustina; Pol'la, Matias Esteban; Cechich, Susana Alejandra
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis.
Fil: Buccella, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; Argentina
Fil: Pol'la, Matias Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; Argentina
Fil: Cechich, Susana Alejandra. Universidad Nacional del Comahue. Facultad de Informatica; Argentina
Materia
AUTOMATIC ANALYSIS
SOFTWARE PRODUCT LINE
VARIABILITY MODELLING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/216263
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Improving Variabilty Analysis through Scenario-Based Incompatibility DetectionBuccella, AgustinaPol'la, Matias EstebanCechich, Susana AlejandraAUTOMATIC ANALYSISSOFTWARE PRODUCT LINEVARIABILITY MODELLINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis.Fil: Buccella, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; ArgentinaFil: Pol'la, Matias Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; ArgentinaFil: Cechich, Susana Alejandra. Universidad Nacional del Comahue. Facultad de Informatica; ArgentinaMultidisciplinary Digital Publishing Institute2022-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/216263Buccella, Agustina; Pol'la, Matias Esteban; Cechich, Susana Alejandra; Improving Variabilty Analysis through Scenario-Based Incompatibility Detection; Multidisciplinary Digital Publishing Institute; Information (Switzerland); 13; 3; 3-2022; 1-272078-2489CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2078-2489/13/3/149info:eu-repo/semantics/altIdentifier/doi/10.3390/info13030149info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:00:27Zoai:ri.conicet.gov.ar:11336/216263instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:00:27.419CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Improving Variabilty Analysis through Scenario-Based Incompatibility Detection
title Improving Variabilty Analysis through Scenario-Based Incompatibility Detection
spellingShingle Improving Variabilty Analysis through Scenario-Based Incompatibility Detection
Buccella, Agustina
AUTOMATIC ANALYSIS
SOFTWARE PRODUCT LINE
VARIABILITY MODELLING
title_short Improving Variabilty Analysis through Scenario-Based Incompatibility Detection
title_full Improving Variabilty Analysis through Scenario-Based Incompatibility Detection
title_fullStr Improving Variabilty Analysis through Scenario-Based Incompatibility Detection
title_full_unstemmed Improving Variabilty Analysis through Scenario-Based Incompatibility Detection
title_sort Improving Variabilty Analysis through Scenario-Based Incompatibility Detection
dc.creator.none.fl_str_mv Buccella, Agustina
Pol'la, Matias Esteban
Cechich, Susana Alejandra
author Buccella, Agustina
author_facet Buccella, Agustina
Pol'la, Matias Esteban
Cechich, Susana Alejandra
author_role author
author2 Pol'la, Matias Esteban
Cechich, Susana Alejandra
author2_role author
author
dc.subject.none.fl_str_mv AUTOMATIC ANALYSIS
SOFTWARE PRODUCT LINE
VARIABILITY MODELLING
topic AUTOMATIC ANALYSIS
SOFTWARE PRODUCT LINE
VARIABILITY MODELLING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis.
Fil: Buccella, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; Argentina
Fil: Pol'la, Matias Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; Argentina
Fil: Cechich, Susana Alejandra. Universidad Nacional del Comahue. Facultad de Informatica; Argentina
description Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis.
publishDate 2022
dc.date.none.fl_str_mv 2022-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/216263
Buccella, Agustina; Pol'la, Matias Esteban; Cechich, Susana Alejandra; Improving Variabilty Analysis through Scenario-Based Incompatibility Detection; Multidisciplinary Digital Publishing Institute; Information (Switzerland); 13; 3; 3-2022; 1-27
2078-2489
CONICET Digital
CONICET
url http://hdl.handle.net/11336/216263
identifier_str_mv Buccella, Agustina; Pol'la, Matias Esteban; Cechich, Susana Alejandra; Improving Variabilty Analysis through Scenario-Based Incompatibility Detection; Multidisciplinary Digital Publishing Institute; Information (Switzerland); 13; 3; 3-2022; 1-27
2078-2489
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2078-2489/13/3/149
info:eu-repo/semantics/altIdentifier/doi/10.3390/info13030149
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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