Journal
JOURNAL OF SYSTEMS AND SOFTWARE
Volume 81, Issue 5, Pages 649-660Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jss.2007.07.040
Keywords
software metrics; defect-prone modules; support vector machines; predictive models
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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.
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