USING DATA REDUCTION TECHNIQUES FOR EFFECTIVE BUG TRIAGE
Sri Vasavi College, Erode Self-Finance Wing, 3rd February 2017. National Conference on Computer and Communication, NCCC’17. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
Download this PDF format
Data mining is the practice of evaluating large pre-existing set of data in order to generate the new proceeds data. Bug triaging is just like a part of mining in which it assign a bug from large data set to the developer. Currently, large amount of economic share of IT sector is spending on manually handling the bugs. Triaging is incredible aspect of development that benefits users and developer alike. It was seen that in numbers of approaches they try to reduce the bugs assigning problem but also suffer from number of problems like economic rise, redundant data, poor productivity etc. To overcome all issues Bud triage with reduction technique is adapted by us. It uses Feature Selection and Instance selection reduction algorithm which will help to reduce the data by scale and dimension and provide us with rich repository of data. This will be beneficial to predict out the correct developer to whom the bug can be assigned. Anatomization (Analysis) of Bug triage will show that how the combination of Feature selection and Instance selection will help to achieve the good set of result.
 J. 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.
 D. _Cubrani_c and G. C. Murphy, “Automatic bug triage using text categorization,” in Proc. 16th Int. Conf. Softw. Eng. Knowl. Eng., Jun. 2004, pp. 92–97.
 P. S. Bishnu and V. Bhattacherjee, “Software fault prediction using quad tree-based k-means clustering algorithm,” IEEE Trans. Knowl. Data Eng., vol. 24, no. 6, pp. 1146–1150, Jun. 2012.
 H. Brighton and C. Mellish, “Advances in instance selection for instance-based learning algorithms,” Data Mining Knowl. Discovery, vol. 6, no. 2, pp. 153–172, Apr. 2002.
 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.
 V. Cerver_on 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., vol. 31, no. 3, pp. 408–413, Jun. 2001.
 M. Grochowski and N. Jankowski, “Comparison of instance selection algorithms ii, results and comments,” in Proc. 7th Int. Conf. Artif. Intell. Softw. Comput., Jun. 2004, pp. 580–585.