.png){kind=icon}
Return
Predicting defect-prone software modules using support vector machines
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jss.2007.07.040.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
software metrics
defect-prone modules
support vector machines
predictive models
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.1
Papers:
5.5K
Citations:
8.4K
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSaveM Pathway and Areas 44 and 45 Are Involved in Stereoscopic Recognition Based on Binocular Disparity.
OAAI
OAAIShareSaveShareSave
View more