Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Web Engineering

ICWE 2012: Web Engineering pp 223–237Cite as

  1. Home
  2. Web Engineering
  3. Conference paper
Evaluating the Impact of a Model-Driven Web Engineering Approach on the Productivity and the Satisfaction of Software Development Teams

Evaluating the Impact of a Model-Driven Web Engineering Approach on the Productivity and the Satisfaction of Software Development Teams

  • Yulkeidi Martínez19,
  • Cristina Cachero20 &
  • Santiago Meliá20 
  • Conference paper
  • 2107 Accesses

  • 4 Citations

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7387)

Abstract

BACKGROUND: Model-Driven Engineering claims a positive impact on software productivity and satisfaction. However, few efforts have been made to collect evidences that assess its true benefits and limitations.

OBJECTIVE: To compare the productivity and satisfaction of junior Web developers during the development of the business layer of a Web 2.0 Application when using either a code-centric, a model-based (UML) or a Model-Driven Engineering approach (OOH4RIA).

RESEARCH METHOD: We designed a full factorial, intra-subject experiment in which 26 subjects, divided into five groups, were asked to develop the same three modules of a Web application, each one using a different method. We measured their productivity and satisfaction with each approach.

RESULTS: The use of Model-Driven Engineering practices seems to significantly increase both productivity and satisfaction of junior Web developers, regardless of the particular application. However, modeling activities that are not accompanied by a strong generation environment make productivity and satisfaction decrease below code-centric practices. Further experimentation is needed to be able to generalize the results to a different population, different languages and tools, different domains and different application sizes.

Keywords

  • Software Development Process
  • Software Development Team
  • Model Drive Development
  • Data Access Object
  • Satisfaction Hypothesis

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Download conference paper PDF

References

  1. CMU/SEI: CMMI Product Development Team, CMMI for Development verion 1.2 (2006)

    Google Scholar 

  2. Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research 2(3), 192–222 (1991)

    CrossRef  Google Scholar 

  3. Fowler, M.: UML distilled: a brief guide to the standard object modeling language, 3rd edn. Addison-Wesley Longman Publishing Co., Inc., Boston (2004)

    Google Scholar 

  4. Kleppe, A.G., Warmer, J., Bast, W.: MDA explained: the model driven architecture: practice and promise. Addison-Wesley Longman Publishing Co., Inc., Boston (2003)

    Google Scholar 

  5. Bruckhaus, T., Madhavii, N.H., Janssen, I., Henshaw, J.: The impact of tools on software productivity. IEEE Software 13(5), 29–38 (2002)

    CrossRef  Google Scholar 

  6. Genero, M., Manso, M.E., Visaggio, A., Canfora, G., Piattini, M.: Building measure-based prediction models for UML class diagram maintainability. Empirical Software Engineering 12(5), 517–549 (2007)

    CrossRef  Google Scholar 

  7. Abrahão, S., Iborra, E., Vanderdonckt, J.: Usability evaluation of user interfaces generated with a model-driven architecture tool. Maturing Usability, 3–32 (2008)

    Google Scholar 

  8. Mellor, S.J., Clark, T., Futagami, T.: Model-driven development: guest editors’ introduction. IEEE Software 20(5), 14–18 (2003)

    CrossRef  Google Scholar 

  9. 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, SEAA 2009, pp. 113–120. IEEE (2009)

    Google Scholar 

  10. Mohagheghi, P.: An Approach for Empirical Evaluation of Model-Driven Engineering in Multiple Dimensions. In: C2M:EEMDD 2010 Workshop- from Code Centric to Model Centric: Evaluating the Effectiveness of MDD, pp. 6–17. CEA LIST Publication (2010)

    Google Scholar 

  11. 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)

    CrossRef  Google Scholar 

  12. Wohlin, C., Runeson, P., Höst, M.: Experimentation in software engineering: an introduction. Springer, Netherlands (2000)

    CrossRef  MATH  Google Scholar 

  13. Zelkowitz, M.V.: An update to experimental models for validating computer technology. Journal of Systems and Software 82(3), 373–376 (2009)

    CrossRef  Google Scholar 

  14. Mohagheghi, P., Dehlen, V.: Where Is the Proof? - A Review of Experiences from Applying MDE in Industry. In: Schieferdecker, I., Hartman, A. (eds.) ECMDA-FA 2008. LNCS, vol. 5095, pp. 432–443. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  15. Abrahão, S., Poels, G.: A family of experiments to evaluate a functional size measurement procedure for Web applications. Journal of Systems and Software 82(2), 253–269 (2009)

    CrossRef  Google Scholar 

  16. Afonso, M., Vogel, R., Teixeira, J.: From code centric to model centric software engineering: practical case study of MDD infusion in a systems integration company (2006)

    Google Scholar 

  17. Krogmann, K., Becker, S.: A Case Study on Model-Driven and Conventional Software Development: The Palladio Editor. Software Engineering, 169–176 (2007)

    Google Scholar 

  18. Staron, M.: Transitioning from code-centric to model-driven industrial projects–empirical studies in industry and academia. Model Driven Software Development: Integrating Quality Assurance (2008)

    Google Scholar 

  19. Kapteijns, T., Jansen, S., Brinkkemper, S., Houët, H., Barendse, R.: A Comparative Case Study of Model Driven Development vs Traditional Development: The Tortoise or the Hare. From code centric to model centric software engineering: Practices, Implications and ROI, 22 (2009)

    Google Scholar 

  20. Mellegård, N., Staron, M.: Distribution of Effort among Software Development Artefacts: An Initial Case Study. In: Bider, I., Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Ukor, R. (eds.) BPMDS 2010 and EMMSAD 2010. LNBIP, vol. 50, pp. 234–246. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  21. Panach, J.: Incorporación de mecanismos de usabilidad en un entorno de producción de software dirigido por modelos. Tesis doctotal, Universidad Politécnica de Valencia (2010)

    Google Scholar 

  22. Kampenes, V., Dyba, T., Hannay, J., Ksjoberg, D.: A systematic review of quasi-experiments in software engineering. Information and Software Technology 51(1), 71–82 (2009)

    CrossRef  Google Scholar 

  23. Perry, D.E., Porter, A.A., Votta, L.G.: Empirical studies of software engineering: a roadmap. In: Proceedings of the Conference on the Future of Software Engineering, pp. 345–355. ACM (2000)

    Google Scholar 

  24. Ambler, S.: Agile Modeling: Effective Practices for eXtreme Programming and the Unified Process. Wiley (2002)

    Google Scholar 

  25. Kruchten, P.: The rational unified process: an introduction. Addison-Wesley Professional (2004)

    Google Scholar 

  26. Meliá, S., Gómez, J., Pérez, S., Díaz, O.: Architectural and technological variability in rich internet applications. IEEE Internet Computing 14(3), 24–32 (2010)

    CrossRef  Google Scholar 

  27. Montgomery, D.C.: Design and analysis of experiments. John Wiley & Sons Inc. (2008)

    Google Scholar 

  28. Plonsky, M.: Psychological Statistics (2009)

    Google Scholar 

  29. Gollapudi, K.: Function points or lines of code?–an insight. Global Microsoft Business Unit, Wipro Technologies (2004)

    Google Scholar 

  30. Seato: Counting Lines of Code in C# (2004)

    Google Scholar 

  31. SPSS Inc. an IBM CompanyHeadquarters: PASW Statistics 18 - Content Guide (2009)

    Google Scholar 

  32. Mauchly, J.W.: Significance test for sphericity of a normal n-variate distribution. The Annals of Mathematical Statistics 11(2), 204–209 (1940)

    CrossRef  MathSciNet  MATH  Google Scholar 

  33. Cook, T.D., Campbell, D.T., Day, A.: Quasi-experimentation: Design & analysis issues for field settings. Houghton Mifflin, Boston (1979)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad Máximo Gómez Báez de Ciego de Ávila, Cuba

    Yulkeidi Martínez

  2. DLSI, Universidad de Alicante, Spain

    Cristina Cachero & Santiago Meliá

Authors
  1. Yulkeidi Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cristina Cachero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Santiago Meliá
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica e Informazione, Politecnico di Milano, Via Ponzio 34/5, 20133, Milano, Italy

    Marco Brambilla

  2. Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Oookayama, 152-8552, Tokyo, Japan

    Takehiro Tokuda

  3. Institut für Informatik, Freie Universität Berlin, Königin-Luise-Strasse 24-26, 14195, Berlin, Germany

    Robert Tolksdorf

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Martínez, Y., Cachero, C., Meliá, S. (2012). Evaluating the Impact of a Model-Driven Web Engineering Approach on the Productivity and the Satisfaction of Software Development Teams. In: Brambilla, M., Tokuda, T., Tolksdorf, R. (eds) Web Engineering. ICWE 2012. Lecture Notes in Computer Science, vol 7387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31753-8_17

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-31753-8_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31752-1

  • Online ISBN: 978-3-642-31753-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature