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...
Guardado en:
Publicado: |
2012
|
---|---|
Materias: | |
Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v7212LNCS_n_p377_Alrajeh http://hdl.handle.net/20.500.12110/paper_03029743_v7212LNCS_n_p377_Alrajeh |
Aporte de: |
id |
paper:paper_03029743_v7212LNCS_n_p377_Alrajeh |
---|---|
record_format |
dspace |
spelling |
paper:paper_03029743_v7212LNCS_n_p377_Alrajeh2023-06-08T15:28:43Z Learning from vacuously satisfiable scenario-based specifications 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 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v7212LNCS_n_p377_Alrajeh 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 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. |
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 |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v7212LNCS_n_p377_Alrajeh http://hdl.handle.net/20.500.12110/paper_03029743_v7212LNCS_n_p377_Alrajeh |
_version_ |
1768544273068720128 |