Effective Bug Triage with Software Data ReductionTechniques Using Clustering Mechanism

R.Nishanth Prabhakar*, K.S. Ranjith**
* Postgraduate, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupathi, Andhra Pradesh, India
** Assistant Professor, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupathi, Andhra Pradesh, India.
Periodicity:June - August'2016
DOI : https://doi.org/10.26634/jcom.4.2.8121

Abstract

Bug triage is the most important step in handling the bugs, which occur during a software process. In manual bug triaging process, the received bug is assigned to a tester or a developer by a triager. Hence, the bugs are received in huge numbers, it is difficult to carry out the manual bug triaging process, and it consumes much resources, both in the form of man hours and the economy. Hence, there is a necessity to reduce the exploitation of resources. Hence, a mechanism is proposed which facilitates much better and efficient triaging process by reducing the size of the bug data sets. The mechanism here involves techniques like clustering techniques and selection techniques. This approach proved more efficient than the manual bug triaging process when compared with bug data sets which were retrieved from the open source bug repository called bugzilla.

Keywords

Bug, Triage, Knowledge, Feature Selection, Instance Selection.

How to Cite this Article?

Prabhakar, R.N., and Ranjith, K.S. (2016). Effective Bug Triage With Software Data Reduction Techniques Using Clustering Mechanism. i-manager’s Journal on Computer Science, 4(2), 14-22. https://doi.org/10.26634/jcom.4.2.8121

References

[1]. Ahmed E. Hassan, (2008). “The Road Ahead for Mining Software Repositories”. Software Analysis and Intelligence Lab (SAIL), School of Computing, Queen's University, Canada.
[2]. Ahmed E. Hassan and Tao Xie, (2010). “Mining Software Engineering Data”. 29 IEEE International Conference on Software Engineering, pp. 172 – 173.
[3]. A. Lamkanfi, et al., (2010). “Predicting the Severity of a Reported Bug”. 27 IEEE Working Conference on Mining Software Repositories, pp. 1–10.
[4]. Davour Cubranic and G.C. Murphy, (2004). “Automatic Bug Triage Using Text Categorization”. Proceedings 16 International Conference on Software Engineering Knowledge Engineering, pp.92-97.
[5]. Dominuque Matter, et al., (2009). “Assigning Bug Reports using a Vocabulary-based Expertise Model of Developers”. Proceedings 6 International Working Conference Mining Software Repositories, pp.131-140.
[6]. Gaeul Jeong, et al., (2009). “Improving Bug Triage with Tossing Graphs”. 7 Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, pp.111-120.
[7]. H. Zhang, et al., (2013). “Predicting Bug-fixing Time: An Empirical Study of Commercial Software Projects”. Proceedings of 35 International Conference Software Engineering, pp.1042-1051.
[8]. Neetu Goyal, et al., (2015). “A Novel Way of Assigning Software Bug Priority Using Supervised Classification on Clustered Bugs Data”. Advances in Intelligent Systems and Computing, pp 493-501.
[9]. J. A. Olvera Lopez, (2010). “A Review of Instance Selection Methods”. Artificial Intelligence Review, Vol. 34, No. 2, pp. 133 - 143.
[10]. John Anvik, et al., (2006). “Who Should Fix this Bug?” Proceedings of the 28 International Conference on Software Engineering, pp.361-370.
[11]. J. Anvik and G. C. Murphy, (2011). “Reducing the Effort of Bug Report Triage: Recommenders for Development-oriented Decisions”. ACM Transactions on Software Engineering and Methodology, Vol. 20,No. 3.
[12]. J. Xuan, et al., (2010). “Automatic Bug Triage using Semi-supervised Text Classification”. Proceedings of 22 International Conference on Software Engineering and Knowledge Engineering, pp. 209–214.
[13]. M. Rogati and Y. Yang, (2002). “High-performing Feature Selection for Text Classification”. Proceedings of the 11 International Conference on Information and Knowledge Management, pp. 659-661.
[14]. R. Nishanth Prabhakar and K.S. Ranjith, (2016). “A Survey on Software Reduction Approach For a Competent Bug Triage”. International Journal of Computational Science, Mathematics and Engineering, pp. 84-87.
[15]. Siliva Breu, et al., (2010). “Information Needs in Bug Reports: Improving Cooperation Between Developers and Users”. Proceedings of ACM Conference Computer Supported Cooperative Work, pp. 301–310.
[16]. Shivakumar Shivaji, et al.,(2013). “Reducing Features to Improve Code Change Based Bug Prediction”. IEEE Transactions on Software Engineering, Vol.39, No.4, pp.552–569.
[17]. Sunghun Kim, et al., (2006). “Memories of Bug Fixes”, Proceedings of ACM SIGSOFT International Symposium on the Foundation of Software Engineering, pp. 35-45.
[18]. T. Xie, et al., (2009). “Data Mining for Software Engineering”. Computers, Vol. 42, No. 8, pp. 55–62.
[19]. T. Zimmermann, et al.,(2010). “What Makes a Good Bug Report?”. IEEE Transactions on Software Engineering, Vol. 36, No. 5, pp. 618-643.
[20]. W. Zou, et al. (2011). “Towards Training Set Reduction for Bug Triage”. Proceedings of 35 Annual IEEE International Conference on Computation and Software Applications, pp.576-581.
[21]. Y. Yang and J. Pedersen, (1997). “A Comparative Study on Feature Selection in Text Categorization”. Proceedings of International Conference on Machine Learning, pp.412-420.
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