Bug Report Classification based on Vector Space Model

doi:10.23940/ijpe.19.08.p6.20712080

Abstract

Abstract: As a vehicle for recording and tracking defects, bug reports provide a basis for solving software quality problems. Currently, software testing is often carried out in a multi-person and parallel state. The integration process of numerous bug reports, such as moving fake or duplication bug reports, is facing severe challenges. Therefore, this paper proposes an automatic detection modus for bug reports based on the vector space model. After pre-processing the bug report, a matching library is created according to the test requirements and test report samples. The vector space model is used to calculate the similarity between the two, and the correctness of the bug report is detected based on this. Experiments with the data of a software test contest show that the modus proposed in this paper can correctly judge most bug reports, effectively improving the efficiency of de-false and de-duplication.

Key words: software testing, vector space model, bug report, quality, natural language processing

Lele Chen, Song Huang, Jinlei Sun, Zhanwei Hui, and Sen Yang. Bug Report Classification based on Vector Space Model [J]. Int J Performability Eng, 2019, 15(8): 2071-2080.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] Eclipse, “The Platform for Open Innovation and Collaboration,”(https://www.eclipse.org/)
[2] Firefox, “A Browser Built by Mozilla,” (http://www.mozilla.org/en-US
[3] N. Battenburg, “Duplicate Bug Reports Considered Harmful. Really?” inProceedings of International Conference on Software Maintenance, pp. 337-345, Beijing, China, October 2008
[4] J. Anvik, L. Hiew,G. C. Murphy, “Who Should Fix This Bug?” inProceedings of the 28th International Conference on Software, pp. 361-370, Shanghai, China, May 2006
[5] S. W. Thomas, “The Impact of Classifier Configuration and Classifier Combination on Bug Localization,” IEEE Transactions on Software Engineering, Vol. 39, No. 10, pp. 1427-1443, October 2013
[6] P. Runeson, M. Alexandersson,O. Nyholm, “Detection of Duplicate Defect Reports using Natural Language Processing,” inProceedings of International Conference on Software Engineering, pp. 499-510, IEEE Computer Society, Minneapolis, MN, USA, May 2007
[7] X. Wang, L. Zhang,T. Xie, “An Approach to Detecting Duplicate Bug Reports using Natural Language and Execution Information,” inProceedings of the 30th International Conference on Software Engineering, pp. 461-470, Leipzig, Germany, May 2008
[8] N. Jalbert and W. Weimer, “Automated Duplicate Detection for Bug Tracking Systems,” inProceedings of the IEEE International Conference on Dependable Systems & Networks with Ftcs & Dcc, pp. 52-61, Anchorage, AK, USA, September 2008
[9] N. Kaushik and L. Tahvildari, “A Comparative Study of the Performance of IR Models on Duplicate Bug Detection,” inProceedings of the Euromicro Conference on Software Maintenance and Reengineering, 2012
[10] S. Deerwester, S. T. Dumais,G. W. Furnas, “Indexing by Latent Semantic Analysis,” Journal of the Association for Information Science & Technology, Vol. 41, No. 6, pp. 391-407, May 2010
[11] T. K. Landauer, D. S. Mcnamara,S. Dennis, “Handbook of Latent Semantic Analysis,” Wiley Interdisciplinary Reviews Cognitive Science, Vol. 4, No. 6, pp. 683-692, June 2014
[12] D. M. Blei, A. Y. Ng,M. I. Jordan, “Latent Dirichlet Allocation,” Journal of Machine Learning Research, Vol. 3, pp. 993-1022, March 2003
[13] P. Kanerva, J. Kristofferson,A. Holst, “Random Indexing of Text Samples for Latent Semantic Analysis,” in Proceedings of the 22nd Annual Conference of the Cognitive Science Society, pp. 103-106, Philadelphia, USA, May 2000
[14] M. P. Grineva, M. N. Grinev,D. Lizorkin, “Extracting Key Terms from Noisy and Multi-Theme Documents,” inProceedings of the 18th International Conference on World Wide Web, pp. 661-670, Madrid, Spain, April 2009
[15] Information Retrieval, “The Method and Means of Finding Information,” (http://en.wikipedia.org/ Information_ retrieval
[16] C. H. Papadimitriou, P. Raghavan,H. Tamaki, “Latent Semantic Indexing: A Probabilistic Analysis,” Journal of Computer and System Sciences, Vol. 61, No. 2, pp. 217-235, January 1998
[17] B. Zheng, D. C. Mclean,X. Lu, “Identifying Biological Concepts from a Protein-Related Corpus with a Probabilistic Topic Model,” BMC Bioinformatics, Vol. 7, No. 58, pp. 147-156, December 2006
[18] H. M. Wallach, “Topic Modeling: Beyond Bag-of-Words,” in Proceedings of International Conference on Machine Learning, pp. 977-984, Pennsylvania, USA, June 2006
[19] O. Baysal, M. W. Godfrey,R. Cohen, “A Bug You Like: A Framework for Automated Assignment of Bugs,” in Proceedings of the IEEE 17th International Conference on Program Comprehension, pp. 297-298, Vancouver, Canada, June 2009
[20] D. Matter, A. Kuhn,O. Nierstrasz, “Assigning Bug reports using a Vocabulary-based Expertise Model of Developers,” inProceedings of the 6th International Working Conference, pp. 131-140, Vancouver, Canada, May 2009
[21] G. Salton, “A Vector Space Model for Automatic Indexing,” Communications of the ACM, Vol. 18, No. 11, pp. 613-620, November 1975
[22] M. Mccandless, E. Hatcher,O. Gospondnetic, “Lucene in Action,” 2^nd Edition, 2010