Predicting defect-prone software modules using support vector machines

Authors:
Karim O. Elish;Mahmoud O. Elish
Affiliations:
Information and Computer Science Department, King Fahd University of Petroleum and Minerals, P.O. Box 1082, Dhahran 31261, Saudi Arabia;Information and Computer Science Department, King Fahd University of Petroleum and Minerals, P.O. Box 1082, Dhahran 31261, Saudi Arabia
Venue:
Journal of Systems and Software
Year:
2008

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Abstract

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.