Abstract:
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.
Registro:
Documento: |
Artículo
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Título: | Learning from vacuously satisfiable scenario-based specifications |
Autor: | Alrajeh, D.; Kramer, J.; Russo, A.; Uchitel, S. |
Ciudad: | Tallinn |
Filiación: | Department of Computing, Imperial College, London, United Kingdom Departamento de Computaciòn, FCEyN, UBA, Argentina
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Palabras clave: | 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 |
Año: | 2012
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Volumen: | 7212 LNCS
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Página de inicio: | 377
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Página de fin: | 393
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DOI: |
http://dx.doi.org/10.1007/978-3-642-28872-2_26 |
Título revista: | 15th International Conference on Fundamental Approaches to Software Engineering, FASE 2012, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2012
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Título revista abreviado: | Lect. Notes Comput. Sci.
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ISSN: | 03029743
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PDF: | https://bibliotecadigital.exactas.uba.ar/download/paper/paper_03029743_v7212LNCS_n_p377_Alrajeh.pdf |
Registro: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v7212LNCS_n_p377_Alrajeh |
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Citas:
---------- APA ----------
Alrajeh, D., Kramer, J., Russo, A. & Uchitel, S.
(2012)
. Learning from vacuously satisfiable scenario-based specifications. 15th International Conference on Fundamental Approaches to Software Engineering, FASE 2012, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2012, 7212 LNCS, 377-393.
http://dx.doi.org/10.1007/978-3-642-28872-2_26---------- CHICAGO ----------
Alrajeh, D., Kramer, J., Russo, A., Uchitel, S.
"Learning from vacuously satisfiable scenario-based specifications"
. 15th International Conference on Fundamental Approaches to Software Engineering, FASE 2012, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2012 7212 LNCS
(2012) : 377-393.
http://dx.doi.org/10.1007/978-3-642-28872-2_26---------- MLA ----------
Alrajeh, D., Kramer, J., Russo, A., Uchitel, S.
"Learning from vacuously satisfiable scenario-based specifications"
. 15th International Conference on Fundamental Approaches to Software Engineering, FASE 2012, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2012, vol. 7212 LNCS, 2012, pp. 377-393.
http://dx.doi.org/10.1007/978-3-642-28872-2_26---------- VANCOUVER ----------
Alrajeh, D., Kramer, J., Russo, A., Uchitel, S. Learning from vacuously satisfiable scenario-based specifications. Lect. Notes Comput. Sci. 2012;7212 LNCS:377-393.
http://dx.doi.org/10.1007/978-3-642-28872-2_26