Learning from vacuously satisfiable scenario-based specifications
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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| Formato: | Artículo publishedVersion |
| Lenguaje: | Inglés |
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_03029743_v7212LNCS_n_p377_Alrajeh |
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paperaa:paper_03029743_v7212LNCS_n_p377_Alrajeh2023-06-12T16:47:22Z Learning from vacuously satisfiable scenario-based specifications Lect. Notes Comput. Sci. 2012;7212 LNCS:377-393 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. 2012 info:eu-repo/semantics/article info:ar-repo/semantics/artículo info:eu-repo/semantics/publishedVersion application/pdf eng 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) |
| language |
Inglés |
| orig_language_str_mv |
eng |
| 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 |
| work_keys_str_mv |
AT alrajehd learningfromvacuouslysatisfiablescenariobasedspecifications AT kramerj learningfromvacuouslysatisfiablescenariobasedspecifications AT russoa learningfromvacuouslysatisfiablescenariobasedspecifications AT uchitels learningfromvacuouslysatisfiablescenariobasedspecifications |
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