Which One Is Better, Simple or Complex Metrics?

Fangjun Wu

doi:10.4236/jcc.2015.311009

Home > Journals > Computer Science & Communications > JCC

Journal of Computer and Communications > Vol.3 No.11, November 2015

Which One Is Better, Simple or Complex Metrics? ()

Fangjun Wu^1,2,3

¹School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China.
²Jiangxi Key Laboratory of Data and Knowledge Engineering, Jiangxi University of Finance and Economics, Nanchang, China.
³High Level Engineering Research Center of Electronic-Commerce, Jiangxi Provincial Colleges and Universities, Nanchang, China.

DOI: 10.4236/jcc.2015.311009 PDF HTML XML 4,855 Downloads 5,282 Views Citations

Abstract

At the early stage of software lifecycle, the complexity measurement of UML class diagrams plays an important role in software development, testing and maintenance, and provides guidance for developing high quality software. In order to study which one is better, simple or complex metrics, this paper analyzes and compares four typical metrics of UML class diagrams from experimental software engineering view points. Understandability, analyzability and maintainability were classified and predicted for 27 class diagrams related to a banking system by means of algorithm C5.0 within the famous toolkit Weka. Results suggest that the performance of simple metrics is not inferior to that of complex metrics, in some cases even better than that of complex metrics.

Keywords

Software Measurement, UML Class Diagram, Empirical Validation, Classification

Share and Cite:

Wu, F. (2015) Which One Is Better, Simple or Complex Metrics?. Journal of Computer and Communications, 3, 52-57. doi: 10.4236/jcc.2015.311009.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Genero, M. (2002) Defining and Validating Metrics for Conceptual Models. Ph.D. Thesis, University of Castilla-La Mancha, Wapedia.
[2]	Zhou, Y.M. (2002) Research on Some Software Measurement Problems. Ph.D. Thesis, Southeast University, Nanjing.
[3]	Yi, T. (2006) Research on UML-Model-Oriented Dependence Analysis and Its Applications. Chinese Science and Technology Press, Hefei.
[4]	Wu, F.J. Measuring Complexities of UML Class Diagrams Based on Data Mining and Complex Networks. Journal of Computational Information Systems, Submit.
[5]	Yi, T., Wu, F.J. and Gan, C.Z. (2004) A Comparison of Metrics for UML Class Diagrams. ACM SIGSOFT Software Engineering Notes, 29, 22-27. http://dx.doi.org/10.1145/1022494.1022523
[6]	Yi, T. and Wu, F.J. (2004) Empirical Analysis of Entropy Distance Metric for UML Class Diagrams. ACM SIGSOFT Software Engineering Notes, 29, 11-16. http://dx.doi.org/10.1145/1022494.1022524
[7]	Yi, T. and Wu, F.J. (2008) A Longitudinal and Comparative Study of Complexity Metrics for UML Class Diagrams through Internet Banking. Proceedings of Management Track within WiCOM: Engineering, Services and Knowledge Management (EMS’2008), Dalian, 12-14 October 2008, 1-4. http://dx.doi.org/10.1109/wicom.2008.2947
[8]	Yi, T. (2010) Comparison Research of Two Typical UML Class Diagram Metrics: Experimental Software Engineering. Proceedings of the International Conference on Computer Ap-plication and System Modeling (ICCASM’2010), Taiyuan, 22-24 October 2010, 86-90.
[9]	Witten, I.H. and Frank, E. (2011) Data Mining: Practical Machine Learning Tools and Techniques. 3rd Edition, Morgan Kaufmann, San Francis-co.
[10]	Baudry, B., Traon, Y.L. and Sunyé, G. (2002) Testability Analysis of a UML Class Diagram. Proceedings of the 8th International Symposium on Software Metrics, Ottawa, 4-7 June 2002, 54-63. http://dx.doi.org/10.1109/metric.2002.1011325
[11]	Huang, J. and Ling, C.X. (2005) Using AUC and Accuracy in Evaluating Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering, 17, 299-310. http://dx.doi.org/10.1109/TKDE.2005.50