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Enhanced Feature Selection Algorithm for Effective Bug Triage Software

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 10))

Abstract

For developing any software application or product it is necessary to find the bug in the product while developing the product. At every phase of testing the bug report is generated, most of the time is wasted for fixing the bug. Software industries waste 45% of cost in fixing the bug. For fixing the bug one of the essential techniques is bug triage. Bug triage is a process for fixing the bugs whose main object is to appropriately allocate a developer to a novel bug for further handling. Initially manual work is needed for every time generating the bug report. After that content categorization methods are functional to behavior regular bug triage. The existing system faces the problem of data reduction in the fixing of bugs automatically. Therefore, there is a need of method which decreases the range also improves the excellence of bug information. Traditional system used CH method for feature selection which is not give accurate result. Therefore, in this paper proposed the method of feature selection by using the Kruskal method. By combining the instance collection and the feature collection algorithms to concurrently decrease the data scale also enhance accuracy of the bug reports in the bug triage. By using Kruskal method remove noisy words in a data set. This method can improve the correctness loss by instance collection.

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References

  1. V. Cerveron 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. pp. 408–413 (2001).

    Google Scholar 

  2. 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, pp. 301–310 (2010).

    Google Scholar 

  3. 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., pp. 139–144 (2011).

    Google Scholar 

  4. Jifeng Xuan, He Jiang, Member, Yan Hu, Zhilei Ren, Weiqin Zou, Zhongxuan Luo, and Xindong Wu: Towards Effective Bug Triage with Software Data Reduction Techniques. In: IEEE transactions on knowledge and data engineering (2015).

    Google Scholar 

  5. H. Zhang, L. Gong, and S. Versteeg: Predicting bug-fixing time: An empirical study of commercial software projects. In: Proc. 35th Int. Conf. Softw. Eng., pp. 1042–1051 (2013).

    Google Scholar 

  6. W. Zou, Y. Hu, J. Xuan, and H. Jiang: Towards training set reduction for bug triage. In: Proc. 35th Annu. IEEE Int. Comput. Soft. Appl. Conf., pp. 576–581(2011).

    Google Scholar 

  7. C. Sun, D. Lo, S. C. Khoo, and J. Jiang: Towards more accurate retrieval of duplicate bug reports. In: Proc. 26th IEEE/ACM Int. Conf. Automated Softw. Eng., pp. 253–262 (2011).

    Google Scholar 

  8. 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., pp. 139–144 (2011).

    Google Scholar 

  9. D. Lo, J. Li, L. Wong, and S. C. Khoo: Mining iterative generators and representative rules for software specification discovery. IEEE Trans. Knowl. Data Eng., pp. 282–296, (2011).

    Google Scholar 

  10. T. Zimmermann, N. Nagappan, P. J. Guo, and B. Murphy: Characterizing and predicting which bugs get reopened. In: Proc. 34th Int. Conf. Softw. Eng., pp. 1074–1083 (2012).

    Google Scholar 

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Correspondence to Jayashri C. Gholap .

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Gholap, J.C., Karlekar, N.P. (2018). Enhanced Feature Selection Algorithm for Effective Bug Triage Software. In: Mishra, D., Nayak, M., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Lecture Notes in Networks and Systems, vol 10. Springer, Singapore. https://doi.org/10.1007/978-981-10-3920-1_5

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  • DOI: https://doi.org/10.1007/978-981-10-3920-1_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3919-5

  • Online ISBN: 978-981-10-3920-1

  • eBook Packages: EngineeringEngineering (R0)

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