Assessing UML design metrics for predicting fault-prone classes in a Java system

Proceedings - International Conference on Software Engineering

Volumn , Issue , 2010, Pages 21-30

  Nugroho, Ariadi ^a   Chaudron, Michel R V ^a   Arisholm, Erik ^b  

^a LEIDEN UNIVERSITY   (Netherlands)

^b SIMULA RESEARCH LABORATORY   (Norway)

Author keywords

Classification; Defect; Fault; Logistic regression; Prediction; Quality

Indexed keywords

CODE METRICS; EARLY PREDICTION; FAULT; FAULT-PRONE; HISTORICAL DATA; JAVA CLASS; JAVA SYSTEM; LEVEL OF DETAIL; LOGISTIC REGRESSIONS; PREDICTION MODEL; PREDICTION QUALITY; SEQUENCE DIAGRAMS; SOFTWARE ARTIFACTS; SOFTWARE ENGINEERS; SOFTWARE MODULES; SOFTWARE PROBLEMS; UML DESIGN;

COMPUTER SOFTWARE SELECTION AND EVALUATION; DATA FLOW ANALYSIS; DEFECTS; DESIGN; FORECASTING; MATHEMATICAL MODELS;

SOFTWARE DESIGN;

EID: 77953742026     PISSN: 02705257     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MSR.2010.5463285     Document Type: Conference Paper

References (31)

1. SPSS Statistics Release 17.0.0 for Macintosh.
2. E. Arisholm and L. C. Briand. Predicting fault-prone components in a java legacy system. In ISESE '06: Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering, pages 8-17, New York, NY, USA, 2006. ACM.
3. E. Arisholm, L. C. Briand, and E. B. Johannessen. A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. Technical report, Simula Research Laboratory, 2008.
4. V. R. Basili, L. C. Briand, and W. L. Melo. A validation of object-oriented design metrics as quality indicators. IEEE Trans. Softw. Eng., 22(10):751-761, 1996.
5. L. Briand, P. Devanbu, and W. Melo. An investigation into coupling measures for C++. In ICSE '97: Proceedings of the 19th international conference on Software engineering, pages 412-421, New York, NY, USA, 1997. ACM Press.
6. L. Briand, W. Melo, and J. Wüst. Assessing the Applicability of Fault-Proneness Models Across Object-Oriented Software Projects. IEEE Transactions on Software Engineering, pages 706-720, 2002.
7. L. C. Briand, J. Wüst, J. W. Daly, and D. V. Porter. Exploring the relationship between design measures and software quality in object-oriented systems. J. Syst. Softw., 51(3):245-273, 2000.
8. C. Catal and B. Diri. A systematic review of software fault prediction studies. Expert Systems with Applications, 36(4):7346-7354, 2009.
9. S. R. Chidamber and C. F. Kemerer. A metrics suite for object oriented design. IEEE Trans. Softw. Eng., 20(6):476-493, 1994.
10. K. El Emam, W. Melo, and J. C. Machado. The prediction of faulty classes using object-oriented design metrics. Journal of Systems and Software, 56(1):63-75, 2001.
11. A. Field. Discovering Statistics Using SPSS. SAGE, London, 2nd edition, 2005.
12. M. Genero, E. Manso, A. Visaggio, G. Canfora, and M. Piattini. Building measure-based prediction models for UML class diagram maintainability. Empirical Software Engineering, 12(5):517-549, 10 2007.
13. T. Gyimothy, R. Ferenc, and I. Siket. Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Trans. Softw. Eng., 31(10):897-910, 2005.
14. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explorations, 11(1), 2009.
15. D.W. Hosmer and S. Lemeshow. Applied Logistic Regression. Wiley-Interscience, 2nd edition, 2000.
16. A. Janes, M. Scotto, W. Pedrycz, B. Russo, M. Stefanovic, and G. Succi. Identification of defect-prone classes in telecommunication software systems using design metrics. Information Sciences, 176(24):3711-3734, 2006.
17. Y. Jiang, B. Cuki, T. Menzies, and N. Bartlow. Comparing design and code metrics for software quality prediction. In Proceedings of the 4th international workshop on Predictor models in software engineering, pages 11-18. ACM New York, NY, USA, 2008.
18. T. Khoshgoftaar and N. Seliya. Comparative assessment of software quality classification techniques: An empirical case study. Empirical Software Engineering, 9(3):229-257, 2004.
19. T. M. Khoshgoftaar and N. Seliya. Fault prediction modeling for software quality estimation: Comparing commonly used techniques. Empirical Softw. Engg., 8(3):255-283, 2003.
20. T. J. McCabe. A complexity measure. IEEE Trans. Softw. Eng., 2(4):308-320, 1976.
21. R. Moser, W. Pedrycz, and G. Succi. Comparative analysis of the efficiency of change metrics and static code attributes for defect prediction. Proceedings of the 30th International Conference on Software Engineering, pages 181-190, 2008.
22. A. Nugroho. Level of detail in UML models and its impact on model comprehension: A controlled experiment. Information and Software Technology, 51(12):1670-1685, 2009.
23. A. Nugroho and M. Chaudron. Evaluating the Impact of UML Modeling on Software Quality: An Industrial Case Study. In Model Driven Engineering Languages and Systems: 12th International Conference, Models 2009, Denver, Co, USA, October 4-9, 2009, Proceedings, page 181. Springer, 2009.
24. A. Nugroho, B. Flaton, and M. R. V. Chaudron. Empirical analysis of the relation between level of detail in UML models and defect density. In Czarnecki, editor, Proceedings of the 11th International Conference on Model Driven Engineering Languages and Systems (MODELS), volume 5301 of LNCS, pages 600-614. Springer-Verlag, 2008.
25. N. Ohlsson and H. Alberg. Predicting fault-prone software modules in telephone switches. IEEE Trans. Softw. Eng., 22(12):886-894, 1996.
26. N. Ohlsson, M. Zhao, and M. Helander. Application of multivariate analysis for software fault prediction. Software Quality Control, 7(1):51-66, 1998. (Pubitemid 128695929)
27. T. J. Ostrand and E. J. Weyuker. How to measure success of fault prediction models. In SOQUA '07: Fourth international workshop on Software quality assurance, pages 25-30, New York, NY, USA, 2007. ACM.
28. SDMetrics. The UML design quality metrics tool, http://www.sdmetrics.com.
29. R. Subramanyam and M. S. Krishnan. Empirical analysis of CK metrics for object-oriented design complexity: Implications for software defects. IEEE Trans. Softw. Eng., 29(4):297-310, 2003.
30. M. Zhao, C. Wohlin, N. Ohlsson, and M. Xie. A comparison between software design and code metrics for the prediction of software fault content. Information and Software Technology, 40(14):801-809, 1998.
31. Y. Zhou and H. Leung. Empirical analysis of object-oriented design metrics for predicting high and low severity faults. IEEE Transactions on Software Engineering, 32(10):771-789, 2006.