An ontological knowledge-based method for handling feature model defects due to dead feature

The specifications of a certain domain are addressed by a portfolio of software products, known as Software Product Line (SPL). Feature Model (FM) supports domain engineering by modeling domain knowledge along with variability among SPL. The quality of FM is one of the significant factors for the su...

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Detalhes bibliográficos
Autores: Bhushan, Megha, Galindo Duarte, José Ángel, Negi, A., Samant, P.
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2024
País:España
Recursos:Universidad de Sevilla (US)
Repositório:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/175144
Acesso em linha:https://hdl.handle.net/11441/175144
https://doi.org/10.1016/j.engappai.2024.109000
Access Level:Acceso aberto
Palavra-chave:Software product line
Dead feature
Knowledge-based method
Knowledge representation
Feature model
Ontology
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spelling An ontological knowledge-based method for handling feature model defects due to dead featureBhushan, MeghaGalindo Duarte, José ÁngelNegi, A.Samant, P.Software product lineDead featureKnowledge-based methodKnowledge representationFeature modelOntologyThe specifications of a certain domain are addressed by a portfolio of software products, known as Software Product Line (SPL). Feature Model (FM) supports domain engineering by modeling domain knowledge along with variability among SPL. The quality of FM is one of the significant factors for the successful SPL in order to attain high quality software products. However, the benefits of SPL can be reduced due to defects in FM. Dead Feature (DF) is one of such defects. Several approaches exist in the literature to detect defects due to DF in FMs. But only a few can handle their sources and solutions which are cumbersome and difficult to understand by humans. An ontological knowledge-based method for handling defects due to DF in FMs is described in this paper. It specifies FM in the form of ontology-based knowledge representation. The rules based on first-order logic are created and implemented using Prolog to detect defects due to DF with sources as well as suggest solutions to resolve these defects. A case study of the product line available on SPLOT repository is utilized for illustrating the proposed work. The experiments are performed with real-world FMs of varied sizes from SPLOT and FMs created with the FeatureIDE tool. The results prove the efficiency, scalability (up to model with 32,000 eatures) and accuracy of the presented method. Therefore, reusability of DFs free knowledge enables deriving defect free products from SPL and eventually enhances the quality of SPL.ElsevierLenguajes y Sistemas Informáticos2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/175144https://doi.org/10.1016/j.engappai.2024.109000reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésEngineering Applications Of Artificial Intelligence, 136, 109000.https://www.sciencedirect.com/science/article/pii/S0952197624011588?via%3Dihubinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1751442026-06-17T12:51:07Z
dc.title.none.fl_str_mv An ontological knowledge-based method for handling feature model defects due to dead feature
title An ontological knowledge-based method for handling feature model defects due to dead feature
spellingShingle An ontological knowledge-based method for handling feature model defects due to dead feature
Bhushan, Megha
Software product line
Dead feature
Knowledge-based method
Knowledge representation
Feature model
Ontology
title_short An ontological knowledge-based method for handling feature model defects due to dead feature
title_full An ontological knowledge-based method for handling feature model defects due to dead feature
title_fullStr An ontological knowledge-based method for handling feature model defects due to dead feature
title_full_unstemmed An ontological knowledge-based method for handling feature model defects due to dead feature
title_sort An ontological knowledge-based method for handling feature model defects due to dead feature
dc.creator.none.fl_str_mv Bhushan, Megha
Galindo Duarte, José Ángel
Negi, A.
Samant, P.
author Bhushan, Megha
author_facet Bhushan, Megha
Galindo Duarte, José Ángel
Negi, A.
Samant, P.
author_role author
author2 Galindo Duarte, José Ángel
Negi, A.
Samant, P.
author2_role author
author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
dc.subject.none.fl_str_mv Software product line
Dead feature
Knowledge-based method
Knowledge representation
Feature model
Ontology
topic Software product line
Dead feature
Knowledge-based method
Knowledge representation
Feature model
Ontology
description The specifications of a certain domain are addressed by a portfolio of software products, known as Software Product Line (SPL). Feature Model (FM) supports domain engineering by modeling domain knowledge along with variability among SPL. The quality of FM is one of the significant factors for the successful SPL in order to attain high quality software products. However, the benefits of SPL can be reduced due to defects in FM. Dead Feature (DF) is one of such defects. Several approaches exist in the literature to detect defects due to DF in FMs. But only a few can handle their sources and solutions which are cumbersome and difficult to understand by humans. An ontological knowledge-based method for handling defects due to DF in FMs is described in this paper. It specifies FM in the form of ontology-based knowledge representation. The rules based on first-order logic are created and implemented using Prolog to detect defects due to DF with sources as well as suggest solutions to resolve these defects. A case study of the product line available on SPLOT repository is utilized for illustrating the proposed work. The experiments are performed with real-world FMs of varied sizes from SPLOT and FMs created with the FeatureIDE tool. The results prove the efficiency, scalability (up to model with 32,000 eatures) and accuracy of the presented method. Therefore, reusability of DFs free knowledge enables deriving defect free products from SPL and eventually enhances the quality of SPL.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/175144
https://doi.org/10.1016/j.engappai.2024.109000
url https://hdl.handle.net/11441/175144
https://doi.org/10.1016/j.engappai.2024.109000
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Engineering Applications Of Artificial Intelligence, 136, 109000.
https://www.sciencedirect.com/science/article/pii/S0952197624011588?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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