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A fine-grained analysis of the support provided by UML class diagrams and ER diagrams during data model maintenance

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Abstract

This paper presents the results of an empirical study aiming at comparing the support provided by ER and UML class diagrams during maintenance of data models. We performed one controlled experiment and two replications that focused on comprehension activities (the first activity in the maintenance process) and another controlled experiment on modification activities related to the implementation of given change requests. The results achieved were analyzed at a fine-grained level aiming at comparing the support given by each single building block of the two notations. Such an analysis is used to identify weaknesses (i.e., building blocks not easy to comprehend) in a notation and/or can justify the need of preferring ER or UML for data modeling. The analysis revealed that the UML class diagrams generally provided a better support for both comprehension and modification activities performed on data models as compared to ER diagrams. Nevertheless, the former has some weaknesses related to three building blocks, i.e., multi-value attribute, composite attribute, and weak entity. These findings suggest that an extension of UML class diagrams should be considered to overcome these weaknesses and improve the support provided by UML class diagrams during maintenance of data models.

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Notes

  1. A representation of the EasyClinic system using both ER and UML is reported in the appendix.

  2. The rule of seven is the generally accepted claim that people can hold approximately seven (plus or minus two) chunks or units of information in their short-term memory at a time [18].

    Table 3 Data models used in each controlled experiment
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  3. The students were assigned to the four groups in a randomly balanced way.

References

  1. Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval. Addison-Wesley, UK (1999)

    Google Scholar 

  2. Basili, V., Caldiera, G.: Rombach. The goal question metric paradigm. Wiley, New York (1994)

  3. Basili, V.R., Selby, R.W., Hutchens, D.H.: Experimentation in software engineering. IEEE Transact. Softw. Eng. 12(7), 758–773 (1986)

    Google Scholar 

  4. Batra, D., Hoffer, J., Bostrom, R.: Comparing representations with relational and eer model. Commun. ACM 33(2), 128–139 (1990)

    Google Scholar 

  5. Bavota, G., Gravino, C., Oliveto, R., De Lucia, A., Tortora, G., Genero, M., Cruz-Lemus, J.: UML vs ER - experimental material. http://distat.unimol.it/reports/ERvsUML/ (2012)

  6. Bock, D., Ryan, T.: Accuracy in modeling with extended entity relationship and object oriented data models. J. Database Manag. 4(4), 30–39 (1993)

    Article  Google Scholar 

  7. Briand, L., Labiche, Y., Di Penta, M., Yan-Bondoc, H.: An experimental investigation of formality in UML-based development. IEEE Transact. Softw. Eng. 31(10), 833–849 (2005)

    Article  Google Scholar 

  8. Brosey, M., Shneiderman, B.: Two experimental comparisons of relational and hierarchical database models. Int. J. Man-Mach. Stud. 10, 625–637 (1978)

    Article  Google Scholar 

  9. Cohen, J.: Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Earlbaum Associates, London (1988)

    MATH  Google Scholar 

  10. Conover, W.J.: Practical nonparametric statistics, 3rd edn. Wiley, New York (1998)

    Google Scholar 

  11. De Lucia, A., Gravino, C., Oliveto, R., Tortora, G.: An experimental comparison of ER and UML class diagrams for data modeling. Empirical Softw. Eng. 15(5), 455–492 (2010)

    Article  Google Scholar 

  12. Decorte, G., Eiger, A., Kroenke, D., Kyte, T.: An object-oriented model for capturing data semantics. In: Proceedings of the Eighth International Conference on Data, Engineering, pp. 126–135 (1992)

  13. Durding, B., Becker, C., Gould, J.: Data org. Human Fact. 19, 1–14 (1977)

    Google Scholar 

  14. Juhn, S., Naumann, J.: The effectiveness of data representation characteristics on user validation. In: Proceedings of the 6th International Conference on, Information Systems, pp. 212–226 (1985)

  15. Kampenes, V.B., Dybå, T., Hannay, J.E., Sjøberg, D.I.K.: A systematic review of effect size in software engineering experiments. Inf. Softw. Technol. 49(11–12), 1073–1086 (2007)

    Article  Google Scholar 

  16. Liao, C., Palvia, P.: The impact of data models and task complexity on end-user performance: an experimental investigation. Int. J. Human Comput. Stud. 52, 831–845 (2000)

    Article  Google Scholar 

  17. Liao, C., Shih, M.: The effects of data models and training degrees on end users’ data representations. MIS Review 8, 1–20 (1998)

    Google Scholar 

  18. Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2), 81–97 (1956)

    Article  Google Scholar 

  19. Navathe, S.B.: Evolution of data modeling for databases. Commun. ACM. 35(9), 112–123 (1992)

    Article  Google Scholar 

  20. Oppenheim, A.N.: Questionnaire design. Pinter Publishers, Interviewing and Attitude Measurement (1992)

    Google Scholar 

  21. Palvia, P.: On end-user computing productivity. Inform. Manag. 21, 217–224 (1991)

    Article  Google Scholar 

  22. Palvia, P., Lio, C., To, P.: The impact of conceptual data models on end-user performance. J. Database Manag. 3(4), 4–15 (1992)

    Google Scholar 

  23. Shoval, P., Frumermann, I.: OO and EER conceptual schemas: a comparison of user comprehension. J. Database Manag. 5(4), 28–38 (1994)

    Google Scholar 

  24. Shoval, P., Shiran, S.: Entity-relationship and object-oriented data modeling–an experimental comparison of design quality. Data Knowl. Eng. 21(3), 297–315 (1997)

    Article  MATH  Google Scholar 

  25. Sirkin, R.M.: Statistics for the social sciences. Sage Publications, California (2005)

    Google Scholar 

  26. Wohlin, C., Runeson, P., Host, M., Ohlsson, M.C., Regnell, B., Wesslen, A.: Experimentation in Software Engineering–an introduction. Kluwer, Dordrecht (2000)

    Book  MATH  Google Scholar 

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Acknowledgments

We would like to thank all the students participated as subjects to the controlled experiments. We would also like to thank the anonymous reviewers for their detailed, constructive, and thoughtful comments that helped us to improve the presentation of the results in this paper. This research has been partially funded by the following projects: ORIGIN (CDTI-MICINN and FEDER,IDI-2010043(1-5)) and GEODAS-BC (Ministerio de Econom’a y Competitividad and FEDER, TIN2012-37493-C03-01).

Author information

Authors and Affiliations

  1. University of Salerno, Fisciano, Salerno, Italy

    Gabriele Bavota, Carmine Gravino, Andrea De Lucia & Genoveffa Tortora

  2. University of Molise, Pesche, Isernia, Italy

    Rocco Oliveto

  3. University of Castilla, La Mancha, Spain

    Marcela Genero & José A. Cruz-Lemus

Authors
  1. Gabriele Bavota
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  2. Carmine Gravino
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  3. Rocco Oliveto
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  4. Andrea De Lucia
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  5. Genoveffa Tortora
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  6. Marcela Genero
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  7. José A. Cruz-Lemus
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Corresponding author

Correspondence to Rocco Oliveto.

Additional information

Communicated by Prof. Tony Clark and Prof. Jon Whittle.

This paper is an extension of the work “Identifying the Weaknesses of UML Class Diagrams during Data Model Comprehension” appeared in the Proceedings of the 14th International Conference on Model Driven Engineering Languages and Systems, Wellington, New Zealand, pages 168–182, 2011. LNCS Press.

Appendix: EasyClinic data models

Appendix: EasyClinic data models

Figures 9 and 10 show the ER and UML models, respectively, of one of the object system used in our study, i.e., EasyClinic.

Fig. 9
figure 9

ER diagram modeling the EasyClinic system

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Fig. 10
figure 10

CD diagram modeling the EasyClinic system

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Bavota, G., Gravino, C., Oliveto, R. et al. A fine-grained analysis of the support provided by UML class diagrams and ER diagrams during data model maintenance. Softw Syst Model 14, 287–306 (2015). https://doi.org/10.1007/s10270-012-0312-6

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