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Reseach Article

Software Defect Classification using Bayesian Classification Techniques

Published on March 2015 by Sakthi Kumaresh, Baskaran R, Meenakshy Sivaguru
International Conference on Communication, Computing and Information Technology
Foundation of Computer Science USA
ICCCMIT2014 - Number 2
March 2015
Authors: Sakthi Kumaresh, Baskaran R, Meenakshy Sivaguru
28316ec2-c9cd-4923-8b7c-a9e6848ee2c6

Sakthi Kumaresh, Baskaran R, Meenakshy Sivaguru . Software Defect Classification using Bayesian Classification Techniques. International Conference on Communication, Computing and Information Technology. ICCCMIT2014, 2 (March 2015), 16-20.

@article{
author = { Sakthi Kumaresh, Baskaran R, Meenakshy Sivaguru },
title = { Software Defect Classification using Bayesian Classification Techniques },
journal = { International Conference on Communication, Computing and Information Technology },
issue_date = { March 2015 },
volume = { ICCCMIT2014 },
number = { 2 },
month = { March },
year = { 2015 },
issn = 0975-8887,
pages = { 16-20 },
numpages = 5,
url = { /proceedings/icccmit2014/number2/19773-7017/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Communication, Computing and Information Technology
%A Sakthi Kumaresh
%A Baskaran R
%A Meenakshy Sivaguru
%T Software Defect Classification using Bayesian Classification Techniques
%J International Conference on Communication, Computing and Information Technology
%@ 0975-8887
%V ICCCMIT2014
%N 2
%P 16-20
%D 2015
%I International Journal of Computer Applications
Abstract

Classifying a defect is an important activity for improving software quality. It is important to classify defects as they contain information regarding the quality of processes and product. The information gathered from defects can be used to track the future projects, and to improve its processes. Considering the need to classify the defect and to gain insight knowledge of defect details, this paper attempts to analyze software defect using Bayes net and Naïve Bayes Classification techniques. It is very difficult to produce defect free software product, however, the main purpose of any software engineering activity is to prevent defects from being introduced in the first place. As fixing of software defects are expensive and time consuming, various defect prediction techniques and defect tracing mechanism are being used to prevent software defects from occurring. The aim of this paper is to show the comparative analysis of software defect classification using Bayes net and Naïve Bayes classification techniques in terms of accuracy, Precision, Recall etc. The Naive Bayesian classifier assumes that all variables contribute toward classification and that they are mutually independent. A Naive Bayesian model leads to a simple prediction framework that gives good result which makes it particularly useful for very large datasets like public NASA MDP repository and the same is experimented in this study. The study revealed that, the performance of Naïve Bayes classification for software defect outperforms Bayes net classification method.

References
  1. Sakthi Kumaresh, R. Baskaran, "Defect Analysis and Prevention for Software Process Quality Improvement" Published in International Journal of Computer Applications. vol. 8 – No 7, October 2010.
  2. D Rodriguez, R Ruiz, M Garre, "Attribute Selection in Software Engineering Datasets for detecting Fault Modules" in 33rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2007) 0-7695-2977—1/07 IEEE.
  3. Baojun, Ma, Karel Dejaeger, Jan Vanthienen, Bart Baesens, "Software Defect Prediction based on Association rule classification" K U Leuven , http://ssrn. com/abstract=1785381
  4. Naheed Azeem, Shazia Usmani, "Analysis of Data Mining Based Software Defect Prediction Techniques" in Global Journal of Computer Science and Technology" Volume 11, Issue 16, September 2011.
  5. Maninderjit Kaur, Dr. Sushil Kumar Garg, 'An approach to Detect and Classify Bugs Using Data Mining Techniques" published in International Journal of Advanced Research in Computer Science and Software Engineering" , volume 4,Issue 7, July 2014.
  6. M. Surendra Naidu, Dr. N. Geethanjali "Optimal Rule Selection Based Defect Classification System using Naïve Bayes Classification" published in International Journal of Engineering Science and Technology, Volume 5, No 08, August 2013.
  7. Hui Wang, "Software Defects Classification Prediction Based on Mining Software Repository" published in Uppsala University, January 2014
  8. Dulal Chandra Sahana, "Software Defect Prediction Based on Classification Rule Mining" Dissertation submitted at National Institutue of Technology, Rourkela, May 2013.
  9. Tao Wang, Weihua Li et al, "Software Defect Prediction Based on Classifiers Ensemble" published in "Journal of Information & Computational Sciences" 8:16 (2011) http://www. joics. com
  10. Mohammad Masudur Rahman, Shamima Yeasmin, 'Adaptive Bug Classification for CVE list using Bayesian Probabilistic Approach' Technical Report submitted in University of Saskatchewan, Canada.
  11. Remco R Bouckaert, Bayesian Network Classifiers in Weka for version 3-5-7, May 2008 http://www. cs. waikato. ac. nz/~remco/weka. bn. pdf
  12. Ruchika Rana, Jyothi Pruthi "Naïve bayes classification" published in International Journal for specific research and development. http://www. ijsrd. com/articles/IJSRDV2I4378. pdf
  13. "Naïve Bayesian" http://www. saedsayad. com/naive_bayesian. html
  14. Cagatay Catal, Banu Diri "A Fault detection Strategy for Software Projects" ISSN 1330-3651 (Print), ISSN 1848-6339 (Online) UDK/UDC 004. 416. 052. 42 http://hrcak. srce. hr/file/143476
Index Terms

Computer Science
Information Sciences

Keywords

Defect Bayesian Classification Defect Prediction Software Quality Naïve Bayes.