Enhanced Feature Selection Algorithm for Effective Bug Triage Software

Authors(2) :-Rohini Wankhede, Prof. Pragati Patil

For building up any software application or item it is important to discover the bug in the item while building up the item. At each period of testing the bug report is created, more often than not is squandered for fixing the bug. Software businesses squander 45 percent of expense in fixing the bug. For fixing the bug one of the basic techniques is bug triage. Bug triage is a process for fixing the bugs whose primary item is to suitably assign a designer to a novel bug for further dealing with. At first manual work is accomplished for each time producing the bug report. After that content categorization techniques are useful to conduct, ordinary bug triage. The current framework faces the issue of data reduction in the fixing of bugs consequently. Consequently, there is a need of a technique which diminishes the range additionally improves the brilliance of bug data. Conventional framework utilized CH strategy for highlight choice which does not give precise outcomes. Along these lines, in this paper proposed the technique for highlight choice by utilizing the Kruskal strategy. By joining the case accumulation and the element gathering calculations to simultaneously diminish the data scale likewise upgrade the precision of the bug reports in the bug triage. By utilizing the Kruskal strategy to expel uproarious words in a data set. This technique can improve the accuracy misfortune by case accumulation.

Authors and Affiliations

Rohini Wankhede
PG Scholar, Department of Computer Science Engineering, Abha-Gaikwad Patil College of Engineering, Nagpur, Maharashtra, India.
Prof. Pragati Patil
Assistant Professor, Department of Computer Science Engineering, Abha-Gaikwad Patil College of Engineering, Nagpur, Maharashtra, India.

Bug, Bug Triage, Kruskal Method, Feature Selection, Instance Selection.

  1. JifengXuan, He Jiang, Member, Yan Hu, ZhileiRen, WeiqinZou, ZhongxuanLuo, and XindongWu: Towards Effective Bug Triage with Software Data Reduction Techniques. In: IEEE transactions on knowledge and data engineering (2015).
  2. H. Zhang, L. Gong, and S. Versteeg: Predicting bug-fixing time: An empirical study of commercial software projects. In: Proc. 35th Int. Conf. Softw. Eng., pp. 1042–1051. (2013)
  3. 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., pp. 576–581(2011).
  4. C. 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., pp. 253–262(2011).
  5. J. W. Park, M. W. Lee, J. Kim, S. W. Hwang, and S. Kim: Costriage: A cost-aware triage algorithm for bug reporting systems. In: Proc. 25th Conf. Artif. Intell, pp. 139–144(2011).
  6. D. Lo, J. Li, L. Wong, and S. C. Khoo: Mining iterative generators and representative rules for software specification discovery. IEEE Trans. Knowl. Data Eng., pp. 282–296, (2011).
  7. T. Zimmermann, N. Nagappan, P. J. Guo, and B. Murphy: Characterizing and predicting which bugs get reopened. In: Proc. 34th Int. Conf. Softw. Eng., pp. 1074–1083(2012).
  8. V. Cerveron and F. J. Ferri: Another move toward the minimum consistent subset: A tabu search approach to the condensed nearest neighbor rule. IEEE Trans. Syst., Man, Cybern. Part B, Cybern. pp. 408-413(2001).
  9. 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, pp. 301-310(2010).
  10. J. W. Park, M. W. Lee, J. Kim, S. W. Hwang, and S. Kim: Costriage: A cost-aware triage algorithm for bug reporting systems. In: Proc. 25th Conf. Artif. Intell, pp. 139-144 (2011).

Publication Details

Published in : Volume 6 | Issue 3 | May-June 2019
Date of Publication : 2019-05-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 53-58
Manuscript Number : IJSRST19639
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Rohini Wankhede, Prof. Pragati Patil, " Enhanced Feature Selection Algorithm for Effective Bug Triage Software", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 6, Issue 3, pp.53-58, May-June-2019.
Journal URL : https://ijsrst.com/IJSRST19639
Citation Detection and Elimination     |      | | BibTeX | RIS | CSV

Article Preview