Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31442
Cognitive Weighted Polymorphism Factor: A Comprehension Augmented Complexity Metric

Authors: T. Francis Thamburaj, A. Aloysius


Polymorphism is one of the main pillars of objectoriented paradigm. It induces hidden forms of class dependencies which may impact software quality, resulting in higher cost factor for comprehending, debugging, testing, and maintaining the software. In this paper, a new cognitive complexity metric called Cognitive Weighted Polymorphism Factor (CWPF) is proposed. Apart from the software structural complexity, it includes the cognitive complexity on the basis of type. The cognitive weights are calibrated based on 27 empirical studies with 120 persons. A case study and experimentation of the new software metric shows positive results. Further, a comparative study is made and the correlation test has proved that CWPF complexity metric is a better, more comprehensive, and more realistic indicator of the software complexity than Abreu’s Polymorphism Factor (PF) complexity metric.

Keywords: Cognitive complexity metric, cognitive weighted polymorphism factor, object-oriented metrics, polymorphism factor, software metrics.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1577


[1] T. G. Mayer, T. Hall, “Measuring OO systems: a critical analysis of the MOOD metrics,” Tools 29, (Procs. Technology of OO Languages & Systems, Europe’ 99), R. Mitchell, A. C. Wills, J. Bosch, B. Meyer (Eds.): Los Alamitos, Ca., USA, IEEE Computer Society, pp. 108–117, 1999.
[2] S. Benlarbi, and W. L. Melo, “Polymorphism measures for early risk prediction,” IEEE Software Engineering, 1999. Proceedings of the 1999 International Conference, pp. 334-344, 1999.
[3] C. Pons, L. Olsina, and M. Prieto, “A formal mechanism for assessing polymorphism in object-oriented systems,” In Quality Software, 2000. Proceedings. First Asia-Pacific Conference on, pp. 53-62. IEEE, 2000.
[4] F. B. Abreu, and W. L. Melo, “Evaluating the impact of object-oriented design on software quality,” Proceedings of the 3rd International Software Metrics Symposium (METRICS'96), IEEE, Berlin, Germany, March, 1996.
[5] S. R. Chidamber, C. F. Kemerer, “Towards a metrics suite for objectoriented design,” Object-Oriented Programming Systems, Languages and Applications (OOPSLA), vol. 26, pp. 197–211, 1991.
[6] L. Wei, and H. Sallie, “Object-oriented metrics that predict maintainability,” Journal of systems and software, vol. 23, no. 2, pp. 111-122, 1993.
[7] F. B. Abreu, and R. Carapuça., “Object-oriented software engineering: Measuring and controlling the development process,” Proceedings of the 4th international conference on software quality. vol. 186, pp. 1-8, 1994.
[8] M. Lorenz, and J. Kidd, “Object oriented software metrics,” Prentice Hall Object-Oriented Series, Englewood Cliffs, N.J., USA, 1994.
[9] L. H. Rosenberg, and L. E. Hyatt, “Software quality metrics for objectoriented environments,” Crosstalk journal, vol. 10, no. 4, pp. 1-16, 1997.
[10] J. Bansiya, and C. G. Davis, “A hierarchical model for object-oriented design quality assessment,” IEEE Transactions on Software Engineering,” vol. 28, no. 1, pp. 4-17, 2002.
[11] D. Wu, L. Chen, Y. Zhou, and B. Xu. “A metrics-based comparative study on object-oriented programming languages,” 2015.
[12] Dufour, Bruno, Karel Driesen, Laurie Hendren, and Clark Verbrugge. “Dynamic metrics for Java,” In ACM SIGPLAN Notices, vol. 38, no. 11, pp. 149-168. ACM, 2003.
[13] P. S. Sandhu, and G. Singh, “Dynamic metrics for polymorphism in object oriented systems,” World Academy of Science, Engineering and Technology, vol. 2, pp. 03-27, 2008.
[14] Y. Wang, and J. Shao, “Measurement of the cognitive functional complexity of software,” Proc. Second IEEE Int. Conf. Cognitive Informatics (ICCI’03), pp. 1-6, 2003.
[15] A. Aloysius, and L. Arockiam, “Cognitive weighted response for a class: A new metric for measuring cognitive complexity of object oriented systems,” International Journal of Advanced Research in Computer Science, vol. 3, no. 4, 2012.
[16] A. Aloysisus, and L. Arockiam, “Coupling complexity metric: A cognitive approach,” International Journal of Information Technology and Computer Science, vol. 4, no. 9, pp. 29-35, 2012,
[17] F. B. Abreu et al, “The Design of Eiffel Programs: Quantitative Evaluation Using the MOOD Metrics,” Proceedings of TOOLS'96, California, Jul. 1996.
[18] N. E. Fenton, and J. Bieman, “Software metrics: A rigorous and practical approach,” 3rd edition. CRC Press, ISBN: 9781439838228, pp. 54, November 2014.
[19] F. Thamburaj, “Validation of cognitive weighted method hiding factor complexity metric,” in International Conference on Advanced Computing (ICAC 2015), International Journal of Applied Engineering Research (IJAER), accepted for publication.
[20] F. B. Abreu, M. Goulao, and R. Estevers, “Toward the design quality evaluation of object-oriented software systems,” Proceedings of the 5th International Conference on Software Quality, Austin, Texas, USA, pp. 44-57. 1995.