Impact of MDE Approaches on the Maintainability of Web Applications: An Experimental Evaluation

  • Yulkeidi Martínez
  • Cristina Cachero
  • Maristella Matera
  • Silvia Abrahao
  • Sergio Luján
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6998)

Abstract

Model-driven Engineering (MDE) approaches are often recognized as a solution to palliate the complexity of software maintainability tasks. However, there is no empirical evidence of their benefits and limitations with respect to code-based maintainability practices. To fill this gap, this paper illustrates the results of an empirical study, involving 44 subjects, in which we compared an MDE methodology, WebML, and a code-based methodology, based on PHP, with respect to the performance and satisfaction of junior software developers while executing analysability, corrective and perfective maintainability tasks on Web applications. Results show that the involved subjects performed better with WebML than with PHP, although they showed a slight preference towards tackling maintainability tasks directly on the source code. Our study also aims at providing a replicable laboratory package that can be used to assess the maintainability of different development methods.

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References

  1. 1.
    Ruiz, F., Polo, M.: Mantenimiento del Software. Grupo Alarcos, Departamento de Informática de la Universidad de Castilla-La Mancha (2007)Google Scholar
  2. 2.
    Coleman, D., Ash, D., Lowther, B., Oman, P.: Using metrics to evaluate software system maintainability. Computer 27(8), 44–49 (2002)CrossRefGoogle Scholar
  3. 3.
    Ameller, D., Gutiérrez, F., Cabot, J.: Dealing with non-functional requirements in model-driven development (2010)Google Scholar
  4. 4.
    López, E.D., González, M., López, M., Iduñate, E.L.: Proceso de Desarrollo de Software Mediante Herramientas MDA. In: CISCI: Conferencia Iberoamericana en Sistemas, Cibernética e Informática (2007)Google Scholar
  5. 5.
    Heijstek, W., Chaudron, M.R.V.: Empirical investigations of model size, complexity and effort in a large scale, distributed model driven development process. In: 35th Euromicro Conference on Software Engineering and Advanced Applications, pp. 113–120. IEEE, Los Alamitos (2009)CrossRefGoogle Scholar
  6. 6.
    Mohagheghi, P.: An Approach for Empirical Evaluation of Model-Driven Engineering in Multiple Dimensions. In: From Code Centric to Model Centric: Evaluating the Effectiveness of MDD (C2M:EEMDD), pp. 6–17. CEA LIST Publication (2010)Google Scholar
  7. 7.
    Glass, R.L.: Matching methodology to problem domain. Communications of the ACM 47(5), 19–21 (2004)CrossRefGoogle Scholar
  8. 8.
    Wohlin, C., Runeson, P., Host, M.: Experimentation in software engineering: an introduction. Springer, Netherlands (2000)CrossRefMATHGoogle Scholar
  9. 9.
    Dyba, T., Kitchenham, B.A., Jorgensen, M.: Evidence-based software engineering for practitioners. IEEE Software 22(1), 58–65 (2005)CrossRefGoogle Scholar
  10. 10.
    Kitchenham, B., Budgen, D., Brereton, P., Turner, M., Charters, S., Linkman, S.: Large-scale software engineering questions-expert opinion or empirical evidence? IET Software 1(5), 161–171 (2007)CrossRefGoogle Scholar
  11. 11.
    Zelkowitz, M.V.: An update to experimental models for validating computer technology. Journal of Systems and Software 82(3), 373–376 (2009)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Vallecillo, A., Koch, N., Cachero, C., Comai, S., Fraternali, P., Garrigós, I., Gómez, J., Kappel, G., Knapp, A., Matera, M., et al.: MDWEnet: A practical approach to achieving interoperability of model-driven Web engineering methods. In: Workshop Proc. of 7th Int. Conf. on Web Engineering (ICWE 2007). Citeseer, Italy (2007)Google Scholar
  14. 14.
    Ceri, S., Fraternali, P., Bongio, A., Brambilla, M., Comai, S., Matera, M.: Morgan Kaufmann series in data management systems: Designing data-intensive Web applications. Morgan Kaufmann Pub., San Francisco (2003)Google Scholar
  15. 15.
    ISO/IEC FCD 25010: Systems and software engineering - Software product. Requirements and Evaluation(SQuaRE) - Quality models for software product quality and system quality in use (2009)Google Scholar
  16. 16.
    Chapin, N., Hale, J.E., Khan, K.M., Ramil, J.F., Tan, W.G.: Types of software evolution and software maintenance. Journal of Software Maintenance and Evolution: Research and Practice 13(1), 3–30 (2001)CrossRefMATHGoogle Scholar
  17. 17.
    Kitchenham, B., Mendes, E., Travassos, G.H.: Cross versus Within-Company Cost Estimation Studies: A Systematic Review. IEEE Transactions on Software Engineering 33(5), 316–329 (2007)CrossRefGoogle Scholar
  18. 18.
    Martinez, Y., Cachero, C., Melia, S.: Evidencia empírica sobre mejoras en productividad y calidad mediante el uso de aproximaciones MDD: un mapeo sistemático de la literatura. REICIS (submitted) (2011)Google Scholar
  19. 19.
    Mellegård, N., Staron, M.: Improving Efficiency of Change Impact Assessment Using Graphical Requirement Specifications: An Experiment. In: Product-Focused Software Process Improvement, pp. 336–350 (2010)Google Scholar
  20. 20.
    Perry, D.E., Porter, A.A., Votta, L.G.: Empirical studies of software engineering: a roadmap. In: The Future of Software Engineering, pp. 345–355. ACM, New York (2000)Google Scholar
  21. 21.
    Moody, D.L.: Dealing with Complexity: A Practical Method for Representing Large Entity Relationship Models (PhD Thesis). Melbourne, Australia: Department of Information Systems, University of Melbourne (2001)Google Scholar
  22. 22.
    Abrahão, S., Mendes, E., Gomez, J., Insfran, E.: A model-driven measurement procedure for sizing web applications: Design, automation and validation. In: Engels, G., Opdyke, B., Schmidt, D.C., Weil, F. (eds.) MODELS 2007. LNCS, vol. 4735, pp. 467–481. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Ceri, S., Fraternali, P., Bongio, A.: Web Modeling Language (WebML): a modeling language for designing Web sites. Computer Networks 33(1-6), 137–157 (2000)CrossRefGoogle Scholar
  24. 24.
    Cook, T.D., Campbell, D.T., Day, A.: Quasi-experimentation: Design & analysis issues for field settings. Houghton Mifflin Boston (1979)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yulkeidi Martínez
    • 1
  • Cristina Cachero
    • 2
  • Maristella Matera
    • 3
  • Silvia Abrahao
    • 4
  • Sergio Luján
    • 2
  1. 1.Universidad Máximo Gómez Báez de Ciego de ÁvilaCuba
  2. 2.Universidad de AlicanteSpain
  3. 3.Politecnico di MilanoItaly
  4. 4.Universidad Politécnica de ValenciaSpain

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