Journal Title : International Journal of Modern Trends in Engineering and Science
Paper Title : EFFECTIVE DATA REDUCTION TECHNIQUES TO MAINTAIN BUG TRIAGE
Volume 03 Issue 12 2016
ISSN no: 2348-3121
Page no: 101-104
Abstract – Software bugs are unavoidable and it is an expensive task for fixing software bugs and over 45 percentage of cost in dealing with software bugs by the software companies. The inevitable step of bug triage is carried out to fix a new bug automatically by applying text classification techniques which reduce the cost and time of manual work. In this paper, the problem of data reduction for bug triage is addressed. The proposed technique combines instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, it extracts attributes from historical bug data sets and builds a predictive model for a new bug data set. The results show that the data reduction can effectively reduce the data scale and improve the accuracy of bug triage. The proposed system provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance. It also focuses on the quality of bug data and can be utilized as a preprocessing technique for bug triage, which improves both data quality and reduces data scale.
Keywords— Mining software repositories, Bug reduction, Feature selection, Instance selection, Bug triage
- Jifeng Xuan, He Jiang, Yan Hu, Zhilei Ren, Weiqin Zou,Zhongxuan Luo, and Xindong Wu “Towards Effective Bug Triage with Software Data Reduction Techniques”, IEEE transactions on knowledge and Data Engineering, vol. 27, no. 1, January 2015.
- 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.
- Anvik and G. C. Murphy, “Reducing the effort of bug report triage: Recommenders for development-oriented decisions,”ACM Trans. Soft. Eng. Methodol., vol. 20, no. 3, article 10, Aug. 2011.
- G. Jeong, S. Kim, and T. Zimmermann, “Improving bug triage with tossing graphs,” in Proc. Joint Meeting 12th Eur. Softw. Eng. Conf. 17th ACM SIGSOFT Symp. Found. Softw. Eng. , Aug. 2009,pp. 111–120.
- 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. , Aug. 2011, pp. 139–144.
- T. Zimmermann, R. Premraj, N. Bettenburg, S. Just, A. Schroter, and C. Weiss, “What makes a good bug report?” IEEE Trans. Softw. Eng., vol. 36, no. 5, pp. 618–643, Oct. 2010.
- 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.
- 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.
- B. Fitzgerald, “The transformation of open source software,” MIS Quart. , vol. 30, no. 3, pp. 587–598, Sep. 2006.
- R. S. Pressman, Software Engineering: A Practitioner’s Approach , 7th ed. New York, NY, USA: McGraw-Hill, 2010.
- R. J. Sandusky, L. Gasser, and G. Ripoche, “Bug report networks: Varieties, strategies, and impacts in an F/OSS development community,” in Proc. 1st Intl. Workshop Mining Softw. Repositories, May 2004.
- J. Xuan, H. Jiang, Z. Ren, and W. Zou, “Developer prioritization in bug repositories,” in Proc. 34th Int. Conf. Softw. Eng., 2012.
- Hong, S. Kim, S. C. Cheung, and C. Bird, “Understanding a developer social network and its evolution,” in Proc. 27th IEEE Int. Conf. Softw. Maintenance, Sep. 2011.
- Lamkanfi, S. Demeyer, E. Giger, and B. Goethals, “Predicting the severity of a reported bug,” in Proc. 7th IEEE Working Conf. Mining Softw. Repositories, May 2010.
- J. A. Olvera-L_opez, J. F. Mart_ınez-Trinidad, and J. A. Carrasco- Ochoa, “Restricted sequential floating search applied to object selection,” in Proc. Int. Conf. Mach. Learn. Data Mining Pattern Recognit.,2007.