Learning from vacuously satisfiable scenario-based specifications

Autores
Alrajeh, D.; Kramer, J.; Russo, A.; Uchitel, S.
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Scenarios and use cases are popular means for supporting requirements elicitation and elaboration. They provide examples of how the system-to-be and its environment can interact. However, such descriptions, when large, are cumbersome to reason about, particularly when they include conditional features such as scenario triggers and use case preconditions. One problem is that they are susceptible to being satisfied vacuously: a system that does not exhibit a scenario's trigger or a use case's precondition, need not provide the behaviour described by the scenario or use case. Vacuously satisfiable scenarios often indicate that the specification is partial and provide an opportunity for further elicitation. They may also indicate conflicting boundary conditions. In this paper we propose a systematic, semi-automated approach for detecting vacuously satisfiable scenarios (using model checking) and computing the scenarios needed to avoid vacuity (using machine learning). © 2012 Springer-Verlag Berlin Heidelberg.
Fuente
Lect. Notes Comput. Sci. 2012;7212 LNCS:377-393
Materia
Requirements elicitation
Scenario-based specifications
Semi-automated
Automated approach
Requirements elicitation
Scenario-based specifications
Model checking
Specifications
Artificial intelligence
Computation theory
Learning systems
Model checking
Specifications
Software engineering
Software engineering
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
paperaa:paper_03029743_v7212LNCS_n_p377_Alrajeh

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repository_id_str 1896
network_name_str Biblioteca Digital (UBA-FCEN)
spelling Learning from vacuously satisfiable scenario-based specificationsAlrajeh, D.Kramer, J.Russo, A.Uchitel, S.Requirements elicitationScenario-based specificationsSemi-automatedAutomated approachRequirements elicitationScenario-based specificationsModel checkingSpecificationsArtificial intelligenceComputation theoryLearning systemsModel checkingSpecificationsSoftware engineeringSoftware engineeringScenarios and use cases are popular means for supporting requirements elicitation and elaboration. They provide examples of how the system-to-be and its environment can interact. However, such descriptions, when large, are cumbersome to reason about, particularly when they include conditional features such as scenario triggers and use case preconditions. One problem is that they are susceptible to being satisfied vacuously: a system that does not exhibit a scenario's trigger or a use case's precondition, need not provide the behaviour described by the scenario or use case. Vacuously satisfiable scenarios often indicate that the specification is partial and provide an opportunity for further elicitation. They may also indicate conflicting boundary conditions. In this paper we propose a systematic, semi-automated approach for detecting vacuously satisfiable scenarios (using model checking) and computing the scenarios needed to avoid vacuity (using machine learning). © 2012 Springer-Verlag Berlin Heidelberg.2012info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12110/paper_03029743_v7212LNCS_n_p377_AlrajehLect. Notes Comput. Sci. 2012;7212 LNCS:377-393reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/ar2025-09-04T09:48:27Zpaperaa:paper_03029743_v7212LNCS_n_p377_AlrajehInstitucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962025-09-04 09:48:29.36Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv Learning from vacuously satisfiable scenario-based specifications
title Learning from vacuously satisfiable scenario-based specifications
spellingShingle Learning from vacuously satisfiable scenario-based specifications
Alrajeh, D.
Requirements elicitation
Scenario-based specifications
Semi-automated
Automated approach
Requirements elicitation
Scenario-based specifications
Model checking
Specifications
Artificial intelligence
Computation theory
Learning systems
Model checking
Specifications
Software engineering
Software engineering
title_short Learning from vacuously satisfiable scenario-based specifications
title_full Learning from vacuously satisfiable scenario-based specifications
title_fullStr Learning from vacuously satisfiable scenario-based specifications
title_full_unstemmed Learning from vacuously satisfiable scenario-based specifications
title_sort Learning from vacuously satisfiable scenario-based specifications
dc.creator.none.fl_str_mv Alrajeh, D.
Kramer, J.
Russo, A.
Uchitel, S.
author Alrajeh, D.
author_facet Alrajeh, D.
Kramer, J.
Russo, A.
Uchitel, S.
author_role author
author2 Kramer, J.
Russo, A.
Uchitel, S.
author2_role author
author
author
dc.subject.none.fl_str_mv Requirements elicitation
Scenario-based specifications
Semi-automated
Automated approach
Requirements elicitation
Scenario-based specifications
Model checking
Specifications
Artificial intelligence
Computation theory
Learning systems
Model checking
Specifications
Software engineering
Software engineering
topic Requirements elicitation
Scenario-based specifications
Semi-automated
Automated approach
Requirements elicitation
Scenario-based specifications
Model checking
Specifications
Artificial intelligence
Computation theory
Learning systems
Model checking
Specifications
Software engineering
Software engineering
dc.description.none.fl_txt_mv Scenarios and use cases are popular means for supporting requirements elicitation and elaboration. They provide examples of how the system-to-be and its environment can interact. However, such descriptions, when large, are cumbersome to reason about, particularly when they include conditional features such as scenario triggers and use case preconditions. One problem is that they are susceptible to being satisfied vacuously: a system that does not exhibit a scenario's trigger or a use case's precondition, need not provide the behaviour described by the scenario or use case. Vacuously satisfiable scenarios often indicate that the specification is partial and provide an opportunity for further elicitation. They may also indicate conflicting boundary conditions. In this paper we propose a systematic, semi-automated approach for detecting vacuously satisfiable scenarios (using model checking) and computing the scenarios needed to avoid vacuity (using machine learning). © 2012 Springer-Verlag Berlin Heidelberg.
description Scenarios and use cases are popular means for supporting requirements elicitation and elaboration. They provide examples of how the system-to-be and its environment can interact. However, such descriptions, when large, are cumbersome to reason about, particularly when they include conditional features such as scenario triggers and use case preconditions. One problem is that they are susceptible to being satisfied vacuously: a system that does not exhibit a scenario's trigger or a use case's precondition, need not provide the behaviour described by the scenario or use case. Vacuously satisfiable scenarios often indicate that the specification is partial and provide an opportunity for further elicitation. They may also indicate conflicting boundary conditions. In this paper we propose a systematic, semi-automated approach for detecting vacuously satisfiable scenarios (using model checking) and computing the scenarios needed to avoid vacuity (using machine learning). © 2012 Springer-Verlag Berlin Heidelberg.
publishDate 2012
dc.date.none.fl_str_mv 2012
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/20.500.12110/paper_03029743_v7212LNCS_n_p377_Alrajeh
url http://hdl.handle.net/20.500.12110/paper_03029743_v7212LNCS_n_p377_Alrajeh
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Lect. Notes Comput. Sci. 2012;7212 LNCS:377-393
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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