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Evaluating the Impact of UML Modeling on Software Quality: An Industrial Case Study

  • Conference paper
Model Driven Engineering Languages and Systems (MODELS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 5795))

Abstract

The contribution of formal modeling approaches in software development has always been a subject of debates. The proponents of model-driven development argue that big upfront designs although require substantial investment will payoff later in the implementation phase in terms of increased productivity and quality. On the other hand, software engineers who are not very keen on modeling perceive the activity as simply a waste of time and money without any real contribution to the final software product. Considering present advancement of model-based software development in software industry, we are challenged to investigate the real contribution of modeling in software development. Therefore, in this paper we report on an empirical investigation on the impact of UML modeling on the quality of software system. In particular, we focus on defect density as a measure of software quality. Based on a significant industrial case study, we have found that the use of UML modeling potentially reduces defect density in software system.

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References

  1. Staron, M., Kuzniarz, L., Wohlin, C.: Empirical assessment of using stereotypes to improve comprehension of UML models: a set of experiments. J. Syst. Softw. 79(5), 727–742 (2006)

    Article  Google Scholar 

  2. Ricca, F., Di Penta, M., Torchiano, M., Tonella, P., Ceccato, M.: The role of experience and ability in comprehension tasks supported by UML stereotypes. In: ICSE 2007: Proceedings of the 29th international conference on Software Engineering, Washington, DC, USA, pp. 375–384. IEEE Computer Society, Los Alamitos (2007)

    Google Scholar 

  3. Genero, M., Cruz-Lemus, J.A., Caivano, D., Abrahao, S., Insfran, E., Carsí, J.A.: Assessing the influence of stereotypes on the comprehension of UML sequence diagrams: A controlled experiment. In: Czarnecki, K., Ober, I., Bruel, J.-M., Uhl, A., Völter, M. (eds.) MODELS 2008. LNCS, vol. 5301, pp. 280–294. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  4. Cruz-Lemus, J., Genero, M., Morasca, S., Piattini, M.: Assessing the the understandability of UML statechart diagrams with composite states - a familiy of empirical studies. Empirical Software Engineering (to appear, 2009)

    Google Scholar 

  5. Briand, L.C., Labiche, Y., Penta, M.D., Yan-Bondoc, H.D.: An experimental investigation of formality in UML-based development. IEEE Transactions on Software Engineering 31(10), 833–849 (2005)

    Article  Google Scholar 

  6. Otero, M.C., Dolado, J.: Evaluation of the comprehension of the dynamic modeling in UML. Information and Software Technology 46(1), 35–53 (2004)

    Article  Google Scholar 

  7. Glezer, C., Last, M., Nachmany, E., Shoval, P.: Quality and comprehension of UML interaction diagrams-an experimental comparison. Information and Software Technology 47(10), 675–692 (2005)

    Article  Google Scholar 

  8. Torchiano, M.: Empirical assessment of UML static object diagrams. In: International Workshop on Program Comprehension, pp. 226–230 (2004)

    Google Scholar 

  9. Arisholm, E., Briand, L.C., Hove, S.E., Labiche, Y.: The impact of UML documentation on software maintenance: An experimental evaluation. IEEE Transactions on Software Engineering 32(6), 365–381 (2006)

    Article  Google Scholar 

  10. Nugroho, A., Flaton, B., Chaudron, M.R.V.: Empirical analysis of the relation between level of detail in UML models and defect density. In: Czarnecki, K., Ober, I., Bruel, J.-M., Uhl, A., Völter, M. (eds.) MODELS 2008. LNCS, vol. 5301, pp. 600–614. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., Wesslén, A.: Experimentation in software engineering: an introduction. Kluwer Academic Publishers, Norwell (2000)

    Book  MATH  Google Scholar 

  12. McCabe, T.J.: A complexity measure. IEEE Trans. Softw. Eng. 2(4), 308–320 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  13. Chidamber, S.R., Kemerer, C.F.: A metrics suite for object oriented design. IEEE Trans. Softw. Eng. 20(6), 476–493 (1994)

    Article  Google Scholar 

  14. Subramanyam, R., Krishnan, M.S.: Empirical analysis of ck metrics for object-oriented design complexity: Implications for software defects. IEEE Trans. Softw. Eng. 29(4), 297–310 (2003)

    Article  Google Scholar 

  15. Khoshgoftaar, T.M., Allen, E.B.: Ordering fault-prone software modules. Software Quality Control 11(1), 19–37 (2003)

    Article  Google Scholar 

  16. SDMetrics: The UML design quality metrics tool, http://www.sdmetrics.com

  17. Rutherford, A.: Introducing ANOVA and ANCOVA: a GLM approach. Sage, Thousand Oaks (2001)

    MATH  Google Scholar 

  18. Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics 18(1), 50–60 (1947)

    Article  MathSciNet  MATH  Google Scholar 

  19. Koru, A.G., Liu, H.: An investigation of the effect of module size on defect prediction using static measures. SIGSOFT Softw. Eng. Notes 30(4), 1–5 (2005)

    Google Scholar 

  20. Fenton, N.E., Neil, M.: A critique of software defect prediction models. IEEE Trans. Softw. Eng. 25(5), 675–689 (1999)

    Article  Google Scholar 

  21. Nugroho, A., Chaudron, M.R.V.: A survey into the rigor of UML use and its perceived impact on quality and productivity. In: ESEM 2008: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement, pp. 90–99. ACM, New York (2008)

    Chapter  Google Scholar 

  22. Nugroho, A., Chaudron, M.R.V.: Managing the quality of UML models in practice. In: Rech, J., Bunse, C. (eds.) Model-Driven Software Development: Integrating Quality Assurance. Information Science Reference, pp. 1–36. IGI Publishing, Hershey (2008)

    Google Scholar 

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Nugroho, A., Chaudron, M.R.V. (2009). Evaluating the Impact of UML Modeling on Software Quality: An Industrial Case Study. In: Schürr, A., Selic, B. (eds) Model Driven Engineering Languages and Systems. MODELS 2009. Lecture Notes in Computer Science, vol 5795. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04425-0_14

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  • DOI: https://doi.org/10.1007/978-3-642-04425-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04424-3

  • Online ISBN: 978-3-642-04425-0

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