Risk-driven revision of requirements models

Requirements incompleteness is often the result of unanticipated adverse conditions which prevent the software and its environment from behaving as expected. These conditions represent risks that can cause severe software failures. The identification and resolution of such risks is therefore a cruci...

Descripción completa

Guardado en:
Detalles Bibliográficos
Publicado: 2016
Materias:
Goal-oriented requirements engineering
Inductive learning
Obstacle analysis
Requirements completeness
Theory revision
Formal logic
Iterative methods
Learning algorithms
Requirements engineering
Risk analysis
Risk assessment
Software engineering
Risks
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02705257_v14-22-May-2016_n_p855_Alrajeh
http://hdl.handle.net/20.500.12110/paper_02705257_v14-22-May-2016_n_p855_Alrajeh
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_02705257_v14-22-May-2016_n_p855_Alrajeh
record_format dspace
spelling paper:paper_02705257_v14-22-May-2016_n_p855_Alrajeh2023-06-08T15:24:37Z Risk-driven revision of requirements models Goal-oriented requirements engineering Inductive learning Obstacle analysis Requirements completeness Theory revision Formal logic Iterative methods Learning algorithms Requirements engineering Risk analysis Risk assessment Software engineering Goal-oriented requirements engineering Inductive learning Obstacle analysis Requirements completeness Theory revision Risks Requirements incompleteness is often the result of unanticipated adverse conditions which prevent the software and its environment from behaving as expected. These conditions represent risks that can cause severe software failures. The identification and resolution of such risks is therefore a crucial step towards requirements completeness. Obstacle analysis is a goal-driven form of risk analysis that aims at detecting missing conditions that can obstruct goals from being satisfied in a given domain, and resolving them. This paper proposes an approach for automatically revising goals that may be under-specified or (partially) wrong to resolve obstructions in a given domain. The approach deploys a learning-based revision methodology in which obstructed goals in a goal model are iteratively revised from traces exemplifying obstruction and non-obstruction occurrences. Our revision methodology computes domain-consistent, obstruction-free revisions that are automatically propagated to other goals in the model in order to preserve the correctness of goal models whilst guaranteeing minimal change to the original model. We present the formal foundations of our learning-based approach, and show that it preserves the properties of our formal framework. We validate it against the benchmarking case study of the London Ambulance Service. © 2016 ACM. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02705257_v14-22-May-2016_n_p855_Alrajeh http://hdl.handle.net/20.500.12110/paper_02705257_v14-22-May-2016_n_p855_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 Goal-oriented requirements engineering
Inductive learning
Obstacle analysis
Requirements completeness
Theory revision
Formal logic
Iterative methods
Learning algorithms
Requirements engineering
Risk analysis
Risk assessment
Software engineering
Goal-oriented requirements engineering
Inductive learning
Obstacle analysis
Requirements completeness
Theory revision
Risks
spellingShingle Goal-oriented requirements engineering
Inductive learning
Obstacle analysis
Requirements completeness
Theory revision
Formal logic
Iterative methods
Learning algorithms
Requirements engineering
Risk analysis
Risk assessment
Software engineering
Goal-oriented requirements engineering
Inductive learning
Obstacle analysis
Requirements completeness
Theory revision
Risks
Risk-driven revision of requirements models
topic_facet Goal-oriented requirements engineering
Inductive learning
Obstacle analysis
Requirements completeness
Theory revision
Formal logic
Iterative methods
Learning algorithms
Requirements engineering
Risk analysis
Risk assessment
Software engineering
Goal-oriented requirements engineering
Inductive learning
Obstacle analysis
Requirements completeness
Theory revision
Risks
description Requirements incompleteness is often the result of unanticipated adverse conditions which prevent the software and its environment from behaving as expected. These conditions represent risks that can cause severe software failures. The identification and resolution of such risks is therefore a crucial step towards requirements completeness. Obstacle analysis is a goal-driven form of risk analysis that aims at detecting missing conditions that can obstruct goals from being satisfied in a given domain, and resolving them. This paper proposes an approach for automatically revising goals that may be under-specified or (partially) wrong to resolve obstructions in a given domain. The approach deploys a learning-based revision methodology in which obstructed goals in a goal model are iteratively revised from traces exemplifying obstruction and non-obstruction occurrences. Our revision methodology computes domain-consistent, obstruction-free revisions that are automatically propagated to other goals in the model in order to preserve the correctness of goal models whilst guaranteeing minimal change to the original model. We present the formal foundations of our learning-based approach, and show that it preserves the properties of our formal framework. We validate it against the benchmarking case study of the London Ambulance Service. © 2016 ACM.
title Risk-driven revision of requirements models
title_short Risk-driven revision of requirements models
title_full Risk-driven revision of requirements models
title_fullStr Risk-driven revision of requirements models
title_full_unstemmed Risk-driven revision of requirements models
title_sort risk-driven revision of requirements models
publishDate 2016
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02705257_v14-22-May-2016_n_p855_Alrajeh
http://hdl.handle.net/20.500.12110/paper_02705257_v14-22-May-2016_n_p855_Alrajeh
_version_ 1768545553204903936

Ejemplares similares