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Enforcement of Conceptual Schema Quality Issues in Current Integrated Development Environments

  • David Aguilera
  • Cristina Gómez
  • Antoni Olivé
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7908)

Abstract

We believe that one of the most effective ways of increasing the quality of conceptual schemas in practice is by using an Integrated Development Environment (IDE) that enforces all relevant quality criteria. With this view, in this paper we analyze the support provided by current IDEs in the enforcement of quality criteria and we compare it with the one that could be provided given the current state of the art. We show that there is a large room for improvement. We introduce the idea of a unified catalog that would include all known quality criteria. We present an initial version of this catalog. We then evaluate the effectiveness of the additional support that could be provided by the current IDEs if they enforced all the quality criteria defined in the catalog. We focus on conceptual schemas written in UML/OCL, although our approach could be applied to other languages.

Keywords

conceptual modeling quality method engineering UML IDE 

References

  1. 1.
    Bolloju, N., Leung, F.S.: Assisting novice analysts in developing quality conceptual models with UML. Commun. ACM 49(7), 108–112 (2006)CrossRefGoogle Scholar
  2. 2.
    Krogstie, J.: Model-Based Development and Evolution of Information Systems – A Quality Approach. Springer (2012)Google Scholar
  3. 3.
    Lindland, O.I., Sindre, G., Sølvberg, A.: Understanding quality in conceptual modeling. IEEE Softw. 11(2), 42–49 (1994)CrossRefGoogle Scholar
  4. 4.
    Moody, D.L.: Theoretical and practical issues in evaluating the quality of conceptual models: current state and future directions. Data Knowl. Eng. 55(3), 243–276 (2005)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Shanks, G., Tansley, E., Weber, R.: Using ontology to validate conceptual models. Commun. ACM 46(10), 85–89 (2003)CrossRefGoogle Scholar
  6. 6.
    Si-Said Cherfi, S., Akoka, J., Comyn-Wattiau, I.: Conceptual modeling quality - from EER to UML schemas evaluation. In: Spaccapietra, S., March, S.T., Kambayashi, Y. (eds.) ER 2002. LNCS, vol. 2503, pp. 414–428. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Bolloju, N., Sugumaran, V.: A knowledge-based object modeling advisor for developing quality object models. Expert Syst. Appl. 39(3), 2893–2906 (2012)CrossRefGoogle Scholar
  8. 8.
    Aguilera, D., Gómez, C., Olivé, A.: A method for the definition and treatment of conceptual schema quality issues. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012 Main Conference 2012. LNCS, vol. 7532, pp. 501–514. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Object Management Group (OMG): Unified Modeling Language (UML), Superstructure – version 2.4.1 (2011)Google Scholar
  10. 10.
    McAllister, A.: Complete rules for n-ary relationship cardinality constraints. Data Knowl. Eng. 27(3), 255–288 (1998)zbMATHCrossRefGoogle Scholar
  11. 11.
    Rumbaugh, J., Jacobson, I., Booch, G.: The Unified Modeling Language Reference Manual, 2nd edn. Addison-Wesley (2005)Google Scholar
  12. 12.
    Mozilla: Open Directory Project (ODP) – List of UML tools, http://www.dmoz.org
  13. 13.
    Davies, I., Green, P., Rosemann, M., Indulska, M., Gallo, S.: How do practitioners use conceptual modeling in practice? Data Knowl. Eng. 58(3), 358–380 (2006)CrossRefGoogle Scholar
  14. 14.
    Blanc, X., Mougenot, A., Mounier, I., Mens, T.: Incremental detection of model inconsistencies based on model operations. In: van Eck, P., Gordijn, J., Wieringa, R. (eds.) CAiSE 2009. LNCS, vol. 5565, pp. 32–46. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Finkelstein, A.C.W., Gabbay, D., Hunter, A., Kramer, J., Nuseibeh, B.: Inconsistency handling in multiperspective specifications. IEEE Trans. Softw. Eng. 20(8), 569–578 (1994)CrossRefGoogle Scholar
  16. 16.
    Spanoudakis, G., Zisman, A.: Inconsistency management in software engineering: Survey and open research issues. In: Handbook of Software Engineering and Knowledge Engineering, pp. 329–380. World Scientific (2001)Google Scholar
  17. 17.
    Aguilera, D., Gómez, C., Olivé, A.: Issue catalog, http://helios.lsi.upc.edu/phd/catalog/issues.php
  18. 18.
    Costal, D., Gómez, C.: On the use of association redefinition in UML class diagrams. In: Embley, D.W., Olivé, A., Ram, S. (eds.) ER 2006. LNCS, vol. 4215, pp. 513–527. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Ambler, S.W.: The Elements of UML 2.0 Style. Cambridge University (2005)Google Scholar
  20. 20.
    Chen, P.: English sentence structure and entity-relationship diagrams. Inf. Sci. (2-3), 127–149 (1983)Google Scholar
  21. 21.
    Hay, D.C.: Data Model Patterns: Conventions of Thought, 1st edn. Dorset House Publishing (1996)Google Scholar
  22. 22.
    Meziane, F., Athanasakis, N., Ananiadou, S.: Generating natural language specifications from UML class diagrams. Requir. Eng. 13(1), 1–18 (2008)CrossRefGoogle Scholar
  23. 23.
    Olivé, A.: Conceptual Modeling of Information Systems. Springer (2007)Google Scholar
  24. 24.
    Fowler, M.: Refactoring: Improving the Design of Existing Code. Addison-Wesley (1999)Google Scholar
  25. 25.
    ArgoUML: ArgoUML, http://argouml.tigris.org
  26. 26.
    SDMetrics: The software design metrics tool for the UML, http://sdmetrics.com

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • David Aguilera
    • 1
  • Cristina Gómez
    • 1
  • Antoni Olivé
    • 1
  1. 1.Department of Service and Information System EngineeringBarcelonaTech – UPCBarcelonaSpain

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