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:	SER
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
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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spelling	todo:paper_03029743_v7212LNCS_n_p377_Alrajeh2023-10-03T15:19:23Z Learning from vacuously satisfiable scenario-based specifications Alrajeh, D. Kramer, J. Russo, A. Uchitel, S. 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 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. SER info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03029743_v7212LNCS_n_p377_Alrajeh
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (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	SER
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
url	http://hdl.handle.net/20.500.12110/paper_03029743_v7212LNCS_n_p377_Alrajeh
work_keys_str_mv	AT alrajehd learningfromvacuouslysatisfiablescenariobasedspecifications AT kramerj learningfromvacuouslysatisfiablescenariobasedspecifications AT russoa learningfromvacuouslysatisfiablescenariobasedspecifications AT uchitels learningfromvacuouslysatisfiablescenariobasedspecifications
_version_	1782026793006399488

Learning from vacuously satisfiable scenario-based specifications

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