DataMock: An Agile Approach for Building Data Models from User Interface Mockups

Autores
Rivero, José Matías; Grigera, Julián; Distante, Damiano; Montero, Francisco; Rossi, Gustavo Héctor
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In modern software development, much time is devoted and much attention is paid to the activity of data modeling and the translation of data models into databases. This has motivated the proposal of different approaches and tools to support this activity, such as semiautomatic approaches that generate data models from requirements artifacts using text analysis and sets of heuristics, among other techniques. However, these approaches still suffer from important limitations, including the lack of support for requirements traceability, the poor support for detecting and solving conflicts in domain-specific requirements, and the considerable effort required for manually checking the generated models. This paper introduces DataMock, an Agile approach that enables the iterative building of data models from requirements specifications, while supporting traceability and allowing inconsistencies detection in data requirements and specifications. The paper also describes how the approach effectively allows improving traceability and reducing errors and effort to build data models in comparison with traditional, state-of-the-art, data modeling approaches.
Laboratorio de Investigación y Formación en Informática Avanzada
Materia
Ciencias Informáticas
Data modeling
Agile methods
Mockups
Annotations
Requirements engineering
Requirements traceability
Model-driven development
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/119009

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network_name_str SEDICI (UNLP)
spelling DataMock: An Agile Approach for Building Data Models from User Interface MockupsRivero, José MatíasGrigera, JuliánDistante, DamianoMontero, FranciscoRossi, Gustavo HéctorCiencias InformáticasData modelingAgile methodsMockupsAnnotationsRequirements engineeringRequirements traceabilityModel-driven developmentIn modern software development, much time is devoted and much attention is paid to the activity of data modeling and the translation of data models into databases. This has motivated the proposal of different approaches and tools to support this activity, such as semiautomatic approaches that generate data models from requirements artifacts using text analysis and sets of heuristics, among other techniques. However, these approaches still suffer from important limitations, including the lack of support for requirements traceability, the poor support for detecting and solving conflicts in domain-specific requirements, and the considerable effort required for manually checking the generated models. This paper introduces DataMock, an Agile approach that enables the iterative building of data models from requirements specifications, while supporting traceability and allowing inconsistencies detection in data requirements and specifications. The paper also describes how the approach effectively allows improving traceability and reducing errors and effort to build data models in comparison with traditional, state-of-the-art, data modeling approaches.Laboratorio de Investigación y Formación en Informática Avanzada2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf663-690http://sedici.unlp.edu.ar/handle/10915/119009enginfo:eu-repo/semantics/altIdentifier/issn/1619-1366info:eu-repo/semantics/altIdentifier/doi/10.1007/s10270-017-0586-9info:eu-repo/semantics/altIdentifier/hdl/11746/10689info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:00:10Zoai:sedici.unlp.edu.ar:10915/119009Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:00:11.174SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv DataMock: An Agile Approach for Building Data Models from User Interface Mockups
title DataMock: An Agile Approach for Building Data Models from User Interface Mockups
spellingShingle DataMock: An Agile Approach for Building Data Models from User Interface Mockups
Rivero, José Matías
Ciencias Informáticas
Data modeling
Agile methods
Mockups
Annotations
Requirements engineering
Requirements traceability
Model-driven development
title_short DataMock: An Agile Approach for Building Data Models from User Interface Mockups
title_full DataMock: An Agile Approach for Building Data Models from User Interface Mockups
title_fullStr DataMock: An Agile Approach for Building Data Models from User Interface Mockups
title_full_unstemmed DataMock: An Agile Approach for Building Data Models from User Interface Mockups
title_sort DataMock: An Agile Approach for Building Data Models from User Interface Mockups
dc.creator.none.fl_str_mv Rivero, José Matías
Grigera, Julián
Distante, Damiano
Montero, Francisco
Rossi, Gustavo Héctor
author Rivero, José Matías
author_facet Rivero, José Matías
Grigera, Julián
Distante, Damiano
Montero, Francisco
Rossi, Gustavo Héctor
author_role author
author2 Grigera, Julián
Distante, Damiano
Montero, Francisco
Rossi, Gustavo Héctor
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Data modeling
Agile methods
Mockups
Annotations
Requirements engineering
Requirements traceability
Model-driven development
topic Ciencias Informáticas
Data modeling
Agile methods
Mockups
Annotations
Requirements engineering
Requirements traceability
Model-driven development
dc.description.none.fl_txt_mv In modern software development, much time is devoted and much attention is paid to the activity of data modeling and the translation of data models into databases. This has motivated the proposal of different approaches and tools to support this activity, such as semiautomatic approaches that generate data models from requirements artifacts using text analysis and sets of heuristics, among other techniques. However, these approaches still suffer from important limitations, including the lack of support for requirements traceability, the poor support for detecting and solving conflicts in domain-specific requirements, and the considerable effort required for manually checking the generated models. This paper introduces DataMock, an Agile approach that enables the iterative building of data models from requirements specifications, while supporting traceability and allowing inconsistencies detection in data requirements and specifications. The paper also describes how the approach effectively allows improving traceability and reducing errors and effort to build data models in comparison with traditional, state-of-the-art, data modeling approaches.
Laboratorio de Investigación y Formación en Informática Avanzada
description In modern software development, much time is devoted and much attention is paid to the activity of data modeling and the translation of data models into databases. This has motivated the proposal of different approaches and tools to support this activity, such as semiautomatic approaches that generate data models from requirements artifacts using text analysis and sets of heuristics, among other techniques. However, these approaches still suffer from important limitations, including the lack of support for requirements traceability, the poor support for detecting and solving conflicts in domain-specific requirements, and the considerable effort required for manually checking the generated models. This paper introduces DataMock, an Agile approach that enables the iterative building of data models from requirements specifications, while supporting traceability and allowing inconsistencies detection in data requirements and specifications. The paper also describes how the approach effectively allows improving traceability and reducing errors and effort to build data models in comparison with traditional, state-of-the-art, data modeling approaches.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/119009
url http://sedici.unlp.edu.ar/handle/10915/119009
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1619-1366
info:eu-repo/semantics/altIdentifier/doi/10.1007/s10270-017-0586-9
info:eu-repo/semantics/altIdentifier/hdl/11746/10689
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
663-690
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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