Learning from vacuously satisfiable scenario-based specifications

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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 featur...

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Autores principales:	Alrajeh, D., Kramer, J., Russo, A., Uchitel, S.
Formato:	Artículo publishedVersion
Publicado:	2012
Materias:	Requirements elicitation Scenario-based specifications Semi-automated Automated approach Model checking Specifications Artificial intelligence Computation theory Learning systems Software engineering
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_03029743_v7212LNCS_n_p377_Alrajeh https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=artiaex&d=paper_03029743_v7212LNCS_n_p377_Alrajeh_oai
Aporte de:	Repositorio Digital de la Universidad de Buenos Aires (UBA) de Universidad de Buenos Aires

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spelling	I28-R145-paper_03029743_v7212LNCS_n_p377_Alrajeh_oai2024-08-16 Alrajeh, D. Kramer, J. Russo, A. Uchitel, S. 2012 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. application/pdf http://hdl.handle.net/20.500.12110/paper_03029743_v7212LNCS_n_p377_Alrajeh info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar Lect. Notes Comput. Sci. 2012;7212 LNCS:377-393 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 Learning from vacuously satisfiable scenario-based specifications info:eu-repo/semantics/article info:ar-repo/semantics/artículo info:eu-repo/semantics/publishedVersion https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=artiaex&d=paper_03029743_v7212LNCS_n_p377_Alrajeh_oai
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-145
collection	Repositorio Digital de la Universidad de Buenos Aires (UBA)
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
spellingShingle	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 Alrajeh, D. Kramer, J. Russo, A. Uchitel, S. Learning from vacuously satisfiable scenario-based specifications
topic_facet	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
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.
format	Artículo Artículo publishedVersion
author	Alrajeh, D. Kramer, J. Russo, A. Uchitel, S.
author_facet	Alrajeh, D. Kramer, J. Russo, A. Uchitel, S.
author_sort	Alrajeh, D.
title	Learning from vacuously satisfiable scenario-based specifications
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
publishDate	2012
url	http://hdl.handle.net/20.500.12110/paper_03029743_v7212LNCS_n_p377_Alrajeh https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=artiaex&d=paper_03029743_v7212LNCS_n_p377_Alrajeh_oai
work_keys_str_mv	AT alrajehd learningfromvacuouslysatisfiablescenariobasedspecifications AT kramerj learningfromvacuouslysatisfiablescenariobasedspecifications AT russoa learningfromvacuouslysatisfiablescenariobasedspecifications AT uchitels learningfromvacuouslysatisfiablescenariobasedspecifications
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Learning from vacuously satisfiable scenario-based specifications

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