Research Article
An Efficient Technique for Software Fault Prediction in Variance Analysis
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 105 - Issue 4 |
| Published: November 2014 |
| Authors: Anuradha S Deokar, V.M.Gaikwad |
10.5120/18366-9510
|
Anuradha S Deokar, V.M.Gaikwad . An Efficient Technique for Software Fault Prediction in Variance Analysis. International Journal of Computer Applications. 105, 4 (November 2014), 27-30. DOI=10.5120/18366-9510
@article{ 10.5120/18366-9510,
author = { Anuradha S Deokar,V.M.Gaikwad },
title = { An Efficient Technique for Software Fault Prediction in Variance Analysis },
journal = { International Journal of Computer Applications },
year = { 2014 },
volume = { 105 },
number = { 4 },
pages = { 27-30 },
doi = { 10.5120/18366-9510 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A Anuradha S Deokar
%A V.M.Gaikwad
%T An Efficient Technique for Software Fault Prediction in Variance Analysis%T
%J International Journal of Computer Applications
%V 105
%N 4
%P 27-30
%R 10.5120/18366-9510
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In this paper, we are using machine learning method for predicting fault, i. e support vector machine to predict the accuracy of the model predicted. The proposed models are validated using dataset collected from Open Source Software. The results are analyzed using Area under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results give you an idea about that the design predict by the support vector machine outperformed the entire the current models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with object oriented metrics and that machine learning methods have a Comparable performance with supervised learning methods.
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