CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

An Efficient Technique for Software Fault Prediction in Variance Analysis

by Anuradha S Deokar, V.m.gaikwad
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 4
Year of Publication: 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 },
issue_date = { November 2014 },
volume = { 105 },
number = { 4 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 27-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number4/18366-9510/ },
doi = { 10.5120/18366-9510 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:36:50.466795+05:30
%A Anuradha S Deokar
%A V.m.gaikwad
%T An Efficient Technique for Software Fault Prediction in Variance Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 4
%P 27-30
%D 2014
%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.

References
  1. J. Demsar. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7,2006.
  2. Y. Jiang, B. Cukic, and Y. Ma. Techniques for evaluating fault prediction models. Empirical Software Engineering, 13(5):561–595, 2008.
  3. Y. Jiang, B. Cukic, and T. Menzies. Fault prediction using early lifecycle data. In The 18th IEEE International Symposium on Software Reliability Engineering, pages 237–246, Nov. 2007.
  4. S. Vanderlooy and E. Hullermeier. A critical analysis of variants of the auc. Machine Learning, 72:247–262, 2008.
  5. I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.
  6. K. Wolter. Introduction to Variance Estimation. Springer Series in Statistics, 2007. T. M. Khoshgoftaar and E. B. Allen. Classification of fault prone software modules: Prior probabilities,costs, and model evaluation. Empirical Software Engineering, 3(3):275–298,1998.
  7. E. Arisholm, L. C. Briand, and E. B. Johannessen. A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. Simula Technical Report, TR-2008-06, 2008.
  8. Yue Jiang, Jie Lin, Bojan Cukic, Tim Menzies . Variance analysis in software fault prediction models. In The 20th International Symposium on Software Reliability Engineering, pages 99–108, Nov. 2009.
  9. Cagatay Catal, Performance Evaluation Metrics for Software, Acta Polytechnica Hungarica , Vol. 9, No. 4, 2012
  10. M. Stone. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society B, 36(1):111–148, 1974.
  11. A. Isaksson, M. Wallman, H. Goransson, and M. Gustafsson. Cross-validation and bootstrapping are unreliable in small sample classification. Pattern Recognition Letters, 29:1960–1965, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machine Fault Prediction Object Oriented ROC curve

Powered by PhDFocusTM