Sciweavers

Share
JSS
2008

Predicting defect-prone software modules using support vector machines

12 years 3 months ago
Predicting defect-prone software modules using support vector machines
Effective prediction of defectprone 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 defectprone 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.
Karim O. Elish, Mahmoud O. Elish
Added 13 Dec 2010
Updated 22 Jan 2012
Type Journal
Year 2008
Where JSS
Authors Karim O. Elish, Mahmoud O. Elish
Comments (0)
books