1. © Predicting Defect-Prone Software Modules Using Support Vector Machines Elish, KO; Elish, MO ELSEVIER SCIENCE INC, JOURNAL OF SYSTEMS AND SOFTWARE; pp: 649-660; Vol: 81 King Fahd University of Petroleum & Minerals http://www.kfupm.edu.sa Summary Effective prediction of defect-prone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Support vector machines (SVM) have been successfully applied for solving both classification and regression problems in many applications. This paper evaluates the capability of SVM in predicting defect-prone software modules and compares its prediction performance against eight statistical and machine learning models in the context of four NASA datasets. The results indicate that the prediction performance of SVM is generally better than, or at least, is competitive against the compared models. (C) 2007 Elsevier Inc. All rights reserved. References: 1. ABE S, 2005, SUPPORT VECTOR MACHI 2. AZAR D, 2002, P 17 IEEE INT C AUT, P285 3. BAO L, 2002, FEBS LETT, V521, P109 4. BASILI VR, 1988, IEEE T SOFTWARE ENG, V14, P758 5. BASILI VR, 1996, IEEE T SOFTWARE ENG, V22, P751 6. BREIMAN L, 2001, MACH LEARN, V45, P5 7. BRIAND LC, 1993, IEEE T SOFTWARE ENG, V19, P1028 8. BURBIDGE R, 2001, COMPUT CHEM, V26, P5 9. BURGES CJC, 1998, DATA MIN KNOWL DISC, V2, P121 10. CAI YD, 2002, COMPUT CHEM, V26, P293 11. CHEN WH, 2005, COMPUT OPER RES, V32, P2617, DOI 12. 10.1016/j.cor.2004.03.019 13. CHIDAMBER SR, 1994, IEEE T SOFTWARE ENG, V20, P476 14. CORTES C, 1995, MACH LEARN, V20, P273 15. CRISTIANINI N, 2000, INTRO SUPPORT VECTOR Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa

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