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
- Institución
- Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
- OAI Identificador
- paperaa:paper_03029743_v7212LNCS_n_p377_Alrajeh
Ver los metadatos del registro completo
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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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12.623145 |