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

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Abstract

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.

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Notes

  1. The 9th Annual State of \(\hbox {Agile}^{\mathrm{TM}}\) Survey. http://www.versionone.com/pdf/2013-state-of-agile-survey.pdf.

  2. TopLink JPA Annotation Reference—http://www.oracle.com/technetwork/middleware/ias/toplink-jpa-annotations-096251.html.

  3. Code First Data Annotations—https://msdn.microsoft.com/en-us/data/jj591583.aspx.

  4. Balsamiq Mockups. http://balsamiqmockups.com.

  5. Pencil Project. http://pencil.evolus.vn.

  6. OCL—http://www.omg.org/spec/OCL/2.4/.

  7. OMG Unified Modeling Language (OMG UML), Infrastructure, Version 2.4.1. http://www.omg.org/spec/UML/2.4.1/Infrastructure/PDF.

  8. XML Metadata Interchange—http://www.omg.org/spec/XMI/.

  9. MagicDraw—http://www.nomagic.com/products/magicdraw.html.

  10. ArgoUML—http://argouml.tigris.org/.

  11. DataMock tooling source code repository—https://bitbucket.org/jmrivero/datamock-tool-public/.

  12. The need for this second iteration might also come from an evolution of the developed application.

  13. \(\mu _{\mathrm{DM}}\) and \(\mu _{\mathrm{M}}\) stand for \(\mu \) values for DataMock and Manual modeling, respectively; \(\upsigma _{\mathrm{DM}}\) and \(\upsigma _{\mathrm{M }}\) stands for \(\upsigma \) values for DataMock and Manual modeling, respectively.

  14. DataMock Stats for BT and ES metrics—https://docs.google.com/spreadsheets/d/1Sv4qLUdI87n23ERMndPSqGZcn4fBqHvfIu4ksUXc_Ts/.

  15. PlantUML: Open-source tool that uses simple textual descriptions to draw UML models—http://plantuml.com/.

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Author information

Authors and Affiliations

  1. LIFIA, Facultad de Informática, Universidad de La Plata, La Plata, Argentina

    José Matías Rivero, Julián Grigera & Gustavo Rossi

  2. Conicet, La Plata, Argentina

    José Matías Rivero & Gustavo Rossi

  3. Unitelma Sapienza University, Rome, Italy

    Damiano Distante

  4. Universidad de Castilla-La Mancha, Albacete, Spain

    Francisco Montero

Authors
  1. José Matías Rivero
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  2. Julián Grigera
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  3. Damiano Distante
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  4. Francisco Montero
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  5. Gustavo Rossi
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Corresponding author

Correspondence to Damiano Distante.

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Communicated by Dr. Benoit Baudry.

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Rivero, J.M., Grigera, J., Distante, D. et al. DataMock: An Agile Approach for Building Data Models from User Interface Mockups. Softw Syst Model 18, 663–690 (2019). https://doi.org/10.1007/s10270-017-0586-9

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