Browse > Article
http://dx.doi.org/10.3745/KTSDE.2013.2.1.001

Predicting Defect-Prone Software Module Using GA-SVM  

Kim, Young-Ok (강릉원주대학교 컴퓨터공학과)
Kwon, Ki-Tae (강릉원주대학교 컴퓨터공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.2, no.1, 2013 , pp. 1-6 More about this Journal
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.
Keywords
Defect-Prone Module; SVM; GA; Classification; Prediction Model; Reliability;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Khoshgoftaar, T.M., Allen, E.B., Kalaichelvan, K.S., Goel, N., "Early quality prediction: a case study in telecommunications", Software, IEEE, Vol.13, Issue 1, pp.65-71, 1996.
2 SungBack Hong, KapSu Kim, YungGun Lee, Chisu Wu, "An Early Software Reliability Prediction Method using Backpropagation Algorithm", Journal of KIISE : Software and Applications, Vol.25, Issue 11, pp.1608-1617, 1998.   과학기술학회마을
3 Ebert, C., "Fuzzy classification for software criticality analysis", Expert Systems with Applications, Vol.11, Issue 3, pp.323-342, 1996.   DOI   ScienceOn
4 ByungRo Moon, "Easily Learning Genetic Algorithms", Hanbit Media, ISBN 978-89-7914-576-2, 2008.
5 Toby Segaran, "Programming collective intelligence", O'relly, 2007.
6 Heesung Lee, Euntai Kim, and Mignon Park, "A genetic feature weighting scheme for pattern recognition", Integrated Computer-Aided Engineering, Vol.14, Issue 2, pp.161-171, 2007.
7 Yu, L., Wang S., Lai K. K., "Mining Stock Market Tendency Using GA-Based Support Vector Machines", Internet and Network Economics, Vol.3828, pp.336-345, 2005.   DOI   ScienceOn
8 Burges, C., "A Tutorial on Support Vector Machines for Pattern Recognition", Data Mining and Knowledge Discovery, Vol.2, Issue 2, pp.121-167, 1998.   DOI   ScienceOn
9 Pang-Ning Tan, Michael Steinbach, Vipin Kumar, "Introduction to data mining", Addison Wesley, 2007.
10 Witten, I., Frank, E., "Data Mining: Practical Machine Learning Tools and Techniques", second ed., Morgan Kaufmann, 2005.
11 Karim O. Elish, Mahmoud O. Elish, "Predicting defect-prone software modules using support vector machines", The Journal of Systems and Software, Vol.81, Issue 5, pp.649-660, 2008.   DOI   ScienceOn