Bug localization is an important task of classification in software programming data set resources. Software programming data is used to find out the related programming codes, similar errors and files. Bug localization ranks the list of possible relevant entities. The bug localization task determines which source code entity is relevant to a particular bug report. In addition, the proposed paper also designs lexical pattern extraction clustering algorithm to classify the bugs in the given bugs report. It measures the semantic similarity between words which is an important component in various tasks on the web, such as relation extraction, community mining, and automatic extraction of metadata. To find out the various semantic relations existing between two given bug sentences, this paper proposes a new pattern extraction algorithm and a pattern clustering algorithm. The proposed method outperforms previously proposed web-based semantic similarity measures on the given data sets. It shows a high correlation with human ratings. Moreover, the above proposed method significantly progresses the accuracy in community mining task.

Bug Localization, Lexical Pattern Extraction, Lexical Pattern Clustering, Information Retrieval

[1] Mozilla Foundation, Bugzilla.2012.
[2] S.K. Lukins, N.A. Kraft, and L.H. Etzkorn, Bug Localization Using Latent Dirichlet Allocation, Information and Software Technology, vol. 52, no. 9,pp. 972-990, 2010.
[3] A.T. Nguyen, T.T. Nguyen, J. Al-Kofahi,H.V. Nguyen, and T.N Nguyen, “A Topic-Based Approach for Narrowing the Search Space of Buggy Files from a Bug Report,” Proc. 26th Int’l Conf Automated Software Eng., pp. 263-272, 2011.
[4] S. Rao and A. Kak, “Retrieval from Software Libraries for Bug Localization: A Comparative Study of Generic and Composite Text Models,” Proc. Eighth Working Conf. Mining Software Repositories, pp. 43-52, 2011.
[5] A.T. Nguyen, T.T. Nguyen, J. Al-Kofahi, H.V. Nguyen, and T.N. Nguyen, “A Topic-Based Approach for Narrowing the Search Space of Buggy Files from a Bug Report,” Proc. 26th Int’l Conf. Automated Software Eng., pp. 263-272, 2011.
[6] G. Salton, A. Wong, and C.S. Yang, “A Vector Space Model for Automatic Indexing,” Comm. ACM, vol. 18, no. 11, pp.613-620, 1975.
[7] S.K. Lukins, N.A. Kraft, and L.H. Etzkorn, Source Code Retrieval for Bug Localization Using Latent Dirichlet Allocation, Proc. 15th Working Conf. Reverse Eng., pp. 155-164, 2008.
[8] D.Poshyvanyk, Y. Gueheneuc, A. Marcus, G.Antoniol, and V. Rajlich, “Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval,” IEEE Trans. Software Eng., vol. 33, no. 6 pp. 420-432, June 2007.
[9] D.Poshyvanyk and A. Marcus, “Combining Formal Concept Analysis with Information Retrieval for Concept Location in Source Code,” Proc. 15th Int’l Conf. Program Comprehension, pp. 37-48, 2007.
[10] B.Cleary, C.Exton, J.Buckley, and M.English, “An Empirical Analysis of Information Retrieval Based Concept Location Techniques in Software Comprehension,” Empirical Software Eng., vol. 14, no. 1, pp. 93-130, 2008.
[11] M. Revelle, B. Dit, and D. Poshyvanyk, Using Data Fusion and Web Mining to Support Feature Location in Software, Proc. 18th Int’l Conf. Program Comprehension, pp. 14-23, 2010.
[12] A.K. McCallum, “Mallet: A Machine Learning for Language Toolkit,” http://mallet.cs.umass.edu, 2002.
[13] Z. Harris, “Distributional Structure,” Word, vol. 10, pp. 146-162, 1954.
[14] J.Ren,M.Harman,M.Di Penta,“ Cooperative Co-evolutionary optimization of software project staff assignments “ in Proc.Int.Symp.Search –Based Sotw. Eng., 2011 , pp. 127-141
[15] C.D.Manning, P.Raghavan and H.Schutze, Introduction to Information retrieval, vol.1, Cambridge Univ. Press Cambridge, 2008.