Evaluating the impact of UML modeling on software quality: An industrial case study

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5795 LNCS, Issue , 2009, Pages 181-195

  Nugroho, Ariadi ^a   Chaudron, Michel R V ^a  

^a LEIDEN UNIVERSITY   (Netherlands)

Author keywords

Case Study; Complexity; Coupling; Defect Density; UML

Indexed keywords

COUPLING DEFECTS; EMPIRICAL INVESTIGATION; FORMAL MODELING; INCREASED PRODUCTIVITY; INDUSTRIAL CASE STUDY; MODEL DRIVEN DEVELOPMENT; MODEL-BASED; QUALITY OF SOFTWARES; SOFTWARE DEVELOPMENT; SOFTWARE ENGINEERS; SOFTWARE INDUSTRY; SOFTWARE PRODUCTS; SOFTWARE QUALITY; SOFTWARE SYSTEMS; SUBSTANTIAL INVESTMENTS; UML MODELING;

DEFECT DENSITY; DEFECTS; FORMAL METHODS; LINGUISTICS; MODELS; RESEARCH; SOFTWARE DESIGN;

COMPUTER SOFTWARE SELECTION AND EVALUATION;

EID: 77249165735     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04425-0_14     Document Type: Conference Paper

References (22)

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)
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)
3. Genero, M., Cruz-Lemus, J.A., Caivano, D., Abrahao, S., Insfran, E., Carsi, 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)
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)
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)
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)
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)
8. Torchiano, M.: Empirical assessment of UML static object diagrams. In: International Workshop on Program Comprehension, pp. 226-230 (2004)
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)
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)
11. Wohlin, C., Runeson, P., Host, M., Ohlsson, M.C., Regnell, B., Wesslén, A.: Experimentation in software engineering: an introduction. Kluwer Academic Publishers, Norwell (2000)
12. McCabe, T.J.: A complexity measure. IEEE Trans. Softw. Eng. 2(4), 308-320 (1976)
13. Chidamber, S.R., Kemerer, C.F.: A metrics suite for object oriented design. IEEE Trans. Softw. Eng. 20(6), 476-493 (1994)
14. Subramanyam, R., Krishnan, M.S.: Empirical analysis of ck metrics for objectoriented design complexity: Implications for software defects. IEEE Trans. Softw. Eng. 29(4), 297-310 (2003)
15. Khoshgoftaar, T.M., Allen, E.B.: Ordering fault-prone software modules. Software Quality Control 11(1), 19-37 (2003)
16. SDMetrics: The UML design quality metrics tool, http://www.sdffiet3rics. com
17. Rutherford, A.: Introducing ANOVA and ANCOVA: a GLM approach. Sage, Thousand Oaks (2001)
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)
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)
20. Fenton, N.E., Neil, M.: A critique of software defect prediction models. IEEE Trans. Softw. Eng. 25(5), 675-689 (1999)
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)
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)