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 Approach for Predicting Bug Triage using Data Reduction Methods

by ShanthiPriya Duraisamy, Laxmi Raja, KalaiSelvi Kandaswamy
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 5
Year of Publication: 2017
Authors: ShanthiPriya Duraisamy, Laxmi Raja, KalaiSelvi Kandaswamy
10.5120/ijca2017915748

ShanthiPriya Duraisamy, Laxmi Raja, KalaiSelvi Kandaswamy . An Approach for Predicting Bug Triage using Data Reduction Methods. International Journal of Computer Applications. 177, 5 ( Nov 2017), 1-6. DOI=10.5120/ijca2017915748

@article{ 10.5120/ijca2017915748,
author = { ShanthiPriya Duraisamy, Laxmi Raja, KalaiSelvi Kandaswamy },
title = { An Approach for Predicting Bug Triage using Data Reduction Methods },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2017 },
volume = { 177 },
number = { 5 },
month = { Nov },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number5/28619-2017915748/ },
doi = { 10.5120/ijca2017915748 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:45:00.620325+05:30
%A ShanthiPriya Duraisamy
%A Laxmi Raja
%A KalaiSelvi Kandaswamy
%T An Approach for Predicting Bug Triage using Data Reduction Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 5
%P 1-6
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Most of the software companies need to deal with large number of software bugs each and every day. Software bugs are inevitable and fixing software bugs is an expensive task. The proposed system employs the combination of data reduction techniques that is feature selection algorithm (FS) and instance selection algorithm (IS) in order to shrink the bug data set and also to upgrade the accuracy of bug triage. Predictive model is used to determine the order of reduction techniques for a new bug data set, i.e., to choose between FS to IS or IS to FS. The aim of effective bug triaging software is to assign potentially skilled developers to new coming bug reports. To decrease the manual and time cost, text classification techniques are applied to accomplish automatic bug triage approach aims to precisely predict the developer to solve or fix the new bug report. The proposed system performance is verified using Mozilla bug data set. To exhibit the effectiveness, scales of data set is reduced by using data reduction technique in order to decrease the time and labor cost, improve the accuracy of bug triage with high-quality bug data in software development and maintenance.

References
  1. J. Anvik, L. Hiew, and G. C. Murphy, “Who should fix this bug?”in Proc. 28th Int. Conf. Softw. Eng., May 2006, pp. 361–370.
  2. S. Breu, R. Premraj, J. Sillito, and T. Zimmermann, “Information needs in bug reports: Improving cooperation between developers and users,” in Proc. ACM Conf. Comput. Supported Cooperative Work, Feb. 2010, pp. 301–310.
  3. Bugzilla, (2015). [Online].Avaialble: http://bugzilla.org/
  4. D.Cubranic and G. C. Murphy, “Automatic bug triage using text categorization,” in Proc. 16th Int. Conf. Softw. Eng. Knowl. Eng., Jun. 2004, pp. 92–97.
  5. N. E. Fenton and S. L. Pfleeger, Software Metrics: A Rigorous and Practical Approach, 2nd ed. Boston, MA, USA: PWS Publishing, 1998.
  6. Y. Freund and R. E. Schapire, “Experiments with a new boosting algorithm,” in Proc. 13th Int. Conf. Mach. Learn., Jul. 1996, pp. 148– 156.
  7. Y. Fu, X. Zhu, and B. Li, “A survey on instance selection for active learning,” Knowl. Inform. Syst., vol. 35, no. 2, pp. 249–283, 2013.
  8. G. Jeong, S. Kim, and T. Zimmermann, “Improving bug triagewith tossing graphs,” in Proc. Joint Meeting 12th Eur. Softw. Eng. Conf. 17th ACM SIGSOFT Symp. Found. Softw. Eng., Aug. 2009, pp. 111–120.
  9. S. Kim, H. Zhang, R. Wu, and L. Gong, “Dealing with noise indefect prediction,” in Proc. 32nd ACM/IEEE Int. Conf. Softw. Eng., May 2010, pp. 481–490.
  10. D. Matter, A. Kuhn, and O. Nierstrasz, “Assigning bug reportsusing a vocabulary-based expertise model of developers,” in Proc. 6th Int. Working Conf. Mining Softw. Repositories, May 2009, pp. 131–140.
  11. Mozilla. (2015). [Online]. Available: http://mozilla.org/
  12. E.Murphy-Hill, T. Zimmermann, C. Bird, and N. Nagappan, “The design of bug fixes,” in Proc. Int. Conf. Softw. Eng., 2013, pp. 332– 341.
  13. M. Rogati and Y. Yang, “High-performing feature selection fortext classification,” in Proc. 11th Int. Conf. Inform.Knowl.Manag., Nov. 2002, pp. 659–661.
  14. S. Shivaji, E. J. Whitehead, Jr., R. Akella, and S. Kim, “Reducing features to improve code change based bug prediction,” IEEE Trans. Soft. Eng., vol. 39, no. 4, pp. 552–569, Apr. 2013.
  15. Sun, D. Lo, S. C. Khoo, and J. Jiang, “Towards more accurate retrieval of duplicate bug reports,” in Proc. 26th IEEE/ACM Int. Conf. Automated Softw. Eng., 2011, pp. 253–262.
  16. X. Wang, L. Zhang, T. Xie, J. Anvik, and J. Sun, “An approach to detecting duplicate bug reports using natural language and execution information,” in Proc. 30th Int. Conf. Softw. Eng., May 2008, pp. 461–470.
  17. D. R. Wilson and T. R. Mart_ınez, “Reduction techniques forinstance-based learning algorithms,” Mach. Learn., vol. 38,pp. 257–286, 2000.
  18. T. Xie, S. Thummalapenta, D. Lo, and C. Liu, “Data mining forsoftware engineering,” Comput., vol. 42, no. 8, pp. 55–62, Aug. 2009.
  19. J. Xuan, H. Jiang, Y. Hu, Z. Ren, Z. Luo, W.Zouand X. Wu, “Towards Effective Bug Triage with SoftwareData Reduction Techniques” in IEEE Trans. on Knowl. and Data Eng., vol. 27, no. 1, Jan. 2015.
  20. J. Xuan, H. Jiang, Z. Ren, and W. Zou, “Developer prioritization in bug repositories,” in Proc. 34th Int. Conf. Softw. Eng., 2012, pp. 25– 35.
  21. J. Xuan, H. Jiang, Z. Ren, J. Yan, and Z. Luo, “Automatic bug triage using semi-supervised text classification,” in Proc. 22nd Int. Conf. Softw. Eng. Knowl. Eng., Jul. 2010, pp. 209–214.
  22. T. Zimmermann, N. Nagappan, P. J. Guo, and B. Murphy, “Characterizing and predicting which bugs get reopened,” in Proc. 34th Int. Conf. Softw. Eng., Jun. 2012, pp. 1074–1083.
  23. W. Zou, Y. Hu, J. Xuan, and H. Jiang, “Towards training set reduction for bug triage,” in Proc. 35th Annu.IEEE Int. Comput. Soft. Appl. Conf., Jul. 2011, pp. 576–581.
Index Terms

Computer Science
Information Sciences

Keywords

Mining Software Repository Data Management in Bug Repository Bug Data Reduction Feature Selection Instance Selection Bug Triage

Powered by PhDFocusTM