Text Classification Techniques to Conduct Automatic Bug Triage

Sunkara Poojitha, M. Sambasiva Rao


We address the issue of information lessening for bug triage, i.e., how to diminish the scale and enhance the nature of bug information. We solidify illustration decision with highlight assurance to in the meantime decrease data scale on the bug estimation and the word estimation. To choose the demand of applying event assurance and highlight decision, we isolate characteristics from recorded bug educational lists and build a judicious model for another bug enlightening gathering. We exactly look at the execution of data reduction on totally 600,000 bug reports of two enormous open source wanders, specifically Eclipse and Mozilla. The results show that our data decline can feasibly lessen the data scale and upgrade the precision of bug triage. Our work gives an approach to manage using frameworks on data taking care of to edge diminished and superb bug data in programming change and support.


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.

S. Artzi, A. Kie_zun, J. Dolby, F. Tip, D. Dig, A. Paradkar, and M. D. Ernst, “Finding bugs in web applications using dynamic test generation and explicit-state model checking,” IEEE Softw., vol. 36, no. 4, pp. 474–494, Jul./Aug. 2010.

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.

C. C. Aggarwal and P. Zhao, “Towards graphical models for text processing,” Knowl. Inform. Syst., vol. 36, no. 1, pp. 1–21, 2013.

Bugzilla, (2014). [Online]. Avaialble: http://bugzilla.org/

K. Balog, L. Azzopardi, and M. de Rijke, “Formal models for expert finding in enterprise corpora,” in Proc. 29th Annu. Int. ACM SIGIR Conf. Res. Develop. Inform. Retrieval, Aug. 2006, pp. 43–50.

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. Bol_on-Canedo, N. S_anchez-Maro~no, and A. Alonso-Betanzos, “A review of feature selection methods on synthetic data,” Knowl. Inform. Syst., vol. 34, no. 3, pp. 483–519, 2013.

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.

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.

Eclipse. (2014). [Online]. Available: http://eclipse.org/ [14] B. Fitzgerald, “The transformation of open source software,” MIS Quart., vol. 30, no. 3, pp. 587–598, Sep. 2006.

A. K. Farahat, A. Ghodsi, M. S. Kamel, “Efficient greedy feature selection for unsupervised learning,” Knowl. Inform. Syst., vol. 35, no. 2, pp. 285–310, May 2013.

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