Increasing the Accuracy of Software Fault Prediction Using Majority Ranking Fuzzy Clustering* - Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing - page 185

rates of software fault prediction approach by our proposed model is found compa-
rable to other existing models and are presented in Tables 1 to 8. According to the
results presented in Tables 1 to 7, in most of the cases, FNR and error rates have
decreased and the results show that our proposed algorithm works as effective as
other software prediction methods.
Acknowledgement. The UniversitiTeknologi Malaysia (UTM) under research grant 03H02
and Ministry of Science, Technology & Innovations Malaysia, under research grant 4S062
are hereby acknowledged for some of the facilities utilized during the course of this
research work.
References
1. http://www.pmhut.com/the-chaos-report-2009-on-it-project-
failure (retrieved August 3, 2013)
2. Hall, T., Beecham, S., Bowes, D., Gray, D., Counsell, S.: A systematic literature re-
view on fault prediction performance in software engineering. IEEE Trans. Softw.
Eng. 38(6) (2011)
3. Catal, C., Sevim, U., Diri, B.: Clustering and metrics thresholds based software fault
prediction of unlabeled program modules. In: Sixth International Conference on
Information Technology: New Generations, ITNG 2009, pp. 199-204 (2009)
4. Zadeh, L.A.: Fuzzy sets. J. Information and Control. 8, 338-353 (1965)
5. Catal, C.: Software fault prediction: A literature review and current trends. J. Expert
Syst. Appl. 38(4), 4626-4636 (2011)
6. Catal, C., Diri, B.: A systematic review of software fault prediction. J. Expert Syst.
Appl. 36, 7346-7354 (2009)
7. Evett, M., Khoshgoftar, T., Chien, P.D., Allen, E.: GP-based software quality predic-
tion. In: Proceedings of the Third Annual Conference Genetic Programming, pp. 60-
65 (1998)
8. Koprinska, I., Poon, J., Clark, J., Chan, J.: Learning to classify e-mail. Inf. Sci 177,
2167-2187 (2007)
9. Thwin, M.M.T., Quah, T.S.: Application of neural networks for software quality pre-
diction using object-oriented metrics. J. Syst. Softw. 76, 147-156 (2005)
10. Menzies, T., Greenwald, J., Frank, A.: Data mining static code attributes to learn de-
fect predictors. IEEE Trans. Softw. Eng. 33(1), 2-13 (2007)
11. El Emam, K., Benlarbi, S., Goel, N., Rai, S.: Comparing case-based reasoning classifi-
ers for predicting high risk software components. J. Syst. Softw. 55(3), 301-320
(2001)
12. Yuan, X., Khoshgoftaar, T.M., Allen, E.B., Ganesan, K.: An application of fuzzy clus-
tering to software quality prediction. In: Proceedings of the Third IEEE Symposium on
Application-Specific Systems and Software Engineering Technology. IEEE Computer
Society, Washington, DC (2000)
13. Catal, C., Diri, B.: Investigating the effect of dataset size, metrics sets, and feature se-
lection techniques on software fault prediction problem. Inf. Sci. 179(8), 1040-1058
(2009)