Predicting Defect-Prone Software Module Using GA-SVM


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 1, pp. 1-6, Jan. 2013
10.3745/KTSDE.2013.2.1.1,   PDF Download:

Abstract

For predicting defect-prone module in software, SVM classifier showed good performance in a previous research. But there are disadvantages that SVM parameter should be chosen differently for every kernel, and algorithm should be performed iteratively for predict results of changed parameter. Therefore, we find these parameters using Genetic Algorithm and compare with result of classification by Backpropagation Algorithm. As a result, the performance of GA-SVM model is better.


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Cite this article
[IEEE Style]
Y. O. Kim and K. T. Kwon, "Predicting Defect-Prone Software Module Using GA-SVM," KIPS Transactions on Software and Data Engineering, vol. 2, no. 1, pp. 1-6, 2013. DOI: 10.3745/KTSDE.2013.2.1.1.

[ACM Style]
Young Ok Kim and Ki Tae Kwon. 2013. Predicting Defect-Prone Software Module Using GA-SVM. KIPS Transactions on Software and Data Engineering, 2, 1, (2013), 1-6. DOI: 10.3745/KTSDE.2013.2.1.1.