We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Software Defect Prediction using Boosting Techniques

by V. Jayaraj, N. Saravana Raman
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 13
Year of Publication: 2013
Authors: V. Jayaraj, N. Saravana Raman
10.5120/10981-6130

V. Jayaraj, N. Saravana Raman . Software Defect Prediction using Boosting Techniques. International Journal of Computer Applications. 65, 13 ( March 2013), 1-4. DOI=10.5120/10981-6130

@article{ 10.5120/10981-6130,
author = { V. Jayaraj, N. Saravana Raman },
title = { Software Defect Prediction using Boosting Techniques },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 13 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number13/10981-6130/ },
doi = { 10.5120/10981-6130 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:18:39.225953+05:30
%A V. Jayaraj
%A N. Saravana Raman
%T Software Defect Prediction using Boosting Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 13
%P 1-4
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Identification and removal of software defects is tedious and time consuming for software development. Improperly planned projects could have defects and the time spent to spot and fix them requires more than the code development time. A reverse engineering sub-area is identification of modules necessitating re-engineering, focusing on faulty modules prediction based on existing information sources like documentation and source code. Predicting defective module is essential in maintenance and reuse by simplifying system working with information and reusable parts localization. In software defect prediction, predictive models estimation is based on code attributes to assess software modules containing errors likelihood. In this paper, the classification accuracy of Boosting techniques for software defect prediction based on the KC1 dataset is investigated.

References
  1. Sommerville. I, Software Engineering, 9th Edition, 2010. Pearson.
  2. Zeller, A. (2009). Why programs fail: a guide to systematic debugging. Morgan Kaufmann.
  3. C. Catal, and B. Diri, "A systematic review of software fault prediction studies," Expert Systems With applications, 36(4):7346-7354, 2009.
  4. Huselius, J. ,Andersson, J. , Hansson, H. ,Punnekkat, S: Automatic Generation and Validation of Models of Legacy Software, 12th IEEE International conference on Embedded and Real-Time Computing Systems and Applications 2006, Sydney, 2006, pp. 342 – 349.
  5. Chia-Chu Chiang,Bayrak, C: Legacy Software Modernization, IEEE International Conference on Systems, Man and Cybernetics, 2006, SMC '06, Taipei, Vol. 2, 2006, pp. 1304-1309.
  6. Baojun, M. , Dejaeger, K. , Vanthienen, J. , & Baesens, B. (2011). Software defect prediction based on association rule classification. Available at SSRN 1785381.
  7. Song, Q. , Jia, Z. , Shepperd, M. , Ying, S. , & Liu, J. (2011). A general software defect-proneness prediction framework. Software Engineering, IEEE Transactions on, 37(3), 356-370.
  8. Li, M. , Zhang, H. , Wu, R. , & Zhou, Z. H. (2012). Sample-based software defect prediction with active and semi-supervised learning. Automated Software Engineering, 19(2), 201-230.
  9. Zhang, H. , Nelson, A. , & Menzies, T. (2010, September). On the value of learning from defect dense components for software defect prediction. InProceedings of the 6th International Conference on Predictive Models in Software Engineering (p. 14). ACM.
  10. Menzies, T. , Milton, Z. , Turhan, B. , Cukic, B. , Jiang, Y. , & Bener, A. (2010). Defect prediction from static code features: current results, limitations, new approaches. Automated Software Engineering, 17(4), 375-407.
  11. Shirabad, J. S. , & Menzies, T. J. (2005). The PROMISE repository of software engineering databases. School of Information Technology and Engineering, University of Ottawa, Canada, 24.
  12. Jerome Friedman, Trevor Hastie and Robert Tibshirani, "Additive Logistic Regression: A Statistical View of Boosting", Annals of Statistcs, Vol. 28, No. 2, pp. 337-407, 2000.
  13. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. (English summary). Ann. Statist, 29(5), 1189-1232.
  14. Friedman, T. Hastie , R. Tibshirani, "Additive Logistic Regression: a Statistical View of Boosting," Ann. Statist. , vol. 28, no. 2, pp. 337-407, 1998
Index Terms

Computer Science
Information Sciences

Keywords

Software Defect Prediction KC1 Dataset Bagging