Browse > Article
http://dx.doi.org/10.3745/KTSDE.2014.3.12.511

A Technique to Recommend Appropriate Developers for Reported Bugs Based on Term Similarity and Bug Resolution History  

Park, Seong Hun (경북대학교 컴퓨터학부)
Kim, Jung Il (경북대학교 컴퓨터학부)
Lee, Eun Joo (경북대학교 컴퓨터학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.3, no.12, 2014 , pp. 511-522 More about this Journal
Abstract
During the development of the software, a variety of bugs are reported. Several bug tracking systems, such as, Bugzilla, MantisBT, Trac, JIRA, are used to deal with reported bug information in many open source development projects. Bug reports in bug tracking system would be triaged to manage bugs and determine developer who is responsible for resolving the bug report. As the size of the software is increasingly growing and bug reports tend to be duplicated, bug triage becomes more and more complex and difficult. In this paper, we present an approach to assign bug reports to appropriate developers, which is a main part of bug triage task. At first, words which have been included the resolved bug reports are classified according to each developer. Second, words in newly bug reports are selected. After first and second steps, vectors whose items are the selected words are generated. At the third step, TF-IDF(Term frequency - Inverse document frequency) of the each selected words are computed, which is the weight value of each vector item. Finally, the developers are recommended based on the similarity between the developer's word vector and the vector of new bug report. We conducted an experiment on Eclipse JDT and CDT project to show the applicability of the proposed approach. We also compared the proposed approach with an existing study which is based on machine learning. The experimental results show that the proposed approach is superior to existing method.
Keywords
Bug Tracking System; Software Repository Mining; Bug Management; Bug Classification; Natural Language Processing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 R. Quinlan, "C4.5: Programs for Machine Learning", Morgan kaufmann, Vol.1, 1993.
2 G. H. John and P. Langley, "Estimating continuous distributions in Bayesian classifiers," in Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, pp.338-345, 1995.
3 S. R. Gunn, "Support Vector Machines for classification and regression," Technical report, University of Southampton, 1998.
4 R. A. Baeza-Yates and B. A. Ribeiro-Neto, "Modern Information Retrieval," Addison-Wesley, Vol.463, 1999.
5 Tian, Yuan, David Lo, and Chengnian Sun, "Drone: Predicting priority of reported bugs by multi-factor analysis," in Proceedings of Software Maintenance (ICSM), 2013 29th IEEE International Conference on, pp. 200-209, 2013.
6 G. Canfora and L. Cerulo, "How software repositories can help in resolving a new change request," in Workshop on Empirical Studies in Reverse Engineering, pp.99-101, 2005.
7 G. Jeong, S. Kim, and T. Zimmermann, "Improving bug triage with bug tossing graphs," in Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, pp.111-120, 2009.
8 P. Bhattacharya, I. Neamtiu, and C. R. Shelton, "Automated, highly-accurate, bug assignment using machine learning and tossing graphs," Transaction on Systems and Software, Vol. 85, No.10, pp.2275-2292, 2012.   DOI
9 P. N. Tan, M. Steinbach and V. Kumar, "Introduction to Data Mining," 1st ed., Addison Wesley, 2005.
10 T. Zimmermann, P. Weissgerber, S. Diel, and A. Zeller, "Mining version histories to guide software changes," IEEE Transactions on Software Engineering, Vol.31, pp.429-445, 2005.   DOI   ScienceOn
11 Alenezi, Mamdouh, Kenneth Magel, and Shadi Banitaan, "Efficient bug triaging using text mining," Journal of Software, Vol.8, No.9, pp.2185-2190, 2013.
12 T. Joachims, "Text categorization with support vector machines: Learning with many relevant features," Springer Berlin Heidelberg, pp.137-142, 1998.
13 L. Hiew, "Assisted detection of duplicate bug reports," M.S. dissertation, University of British Columbia, Vancouver, 2006.
14 P. Runeson, M. Alexandersson, and O. Nyholm, "Detection of duplicate defect reports using natural language processing," in Proceedings of the 29th International Conference on Software Engineering, pp.499-510, 2007.
15 X. Wang, L. Zhang, T. Xie, J. Anvik, and J. Sun, "An approach to detecting duplicate bug reports using natural language and execution information," in Proceedings of the 30th International Conference on Software Engineering, pp.461-470, 2008.
16 E. S. Raymond, "The cathedral and the bazaar", Transactions on Knowledge, Technology & Policy, Vol.12, pp.23-49, 1998.
17 J. Anvik, L. Hiew, and G. C. Murphy, "Who should fix this bug?", in Proceedings of the 28th international conference on Software engineering, pp.361-370, 2006.
18 Sureka, Ashish, and Pankaj Jalote, "Detecting duplicate bug report using character n-gram-based features," in Proceedings of Software Engineering Conference (APSEC), pp.366-374, 2010.
19 Kim, Sunghun, E. James Whitehead, and Yi Zhang, "Classifying software changes: Clean or buggy?," IEEE Transactions on Software Engineering: Vol.34, Issue.2, pp. 181-196, 2008.   DOI
20 Jalbert, Nicholas, and Westley Weimer, "Automated duplicate detection for bug tracking systems," in Proceedings of Dependable Systems and Networks With FTCS and DCC, pp.52-61, 2008.
21 A. Lamkanfi, S. Demeyer, E. Giger, and B. Goethals, "Predicting the severity of a reported bug," in Proceedings of the Working Conference on Mining Software Repositories, pp.1-10, 2010.
22 Shihab, Emad, et al., "Predicting re-opened bugs: A case study on the eclipse project," in Proceedings of Reverse Engineering (WCRE), pp.249-258, 2010.
23 Valdivia Garcia, Harold, and Emad Shihab, "Characterizing and predicting blocking bugs in open source projects," in Proceedings of the 11th Working Conference on Mining Software Repositories, pp.72-81, 2014.
24 D. Cubranic and G. C. Murphy, "Automatic bug triage using text categorization," in Proceedings of the Sixteenth International Conference on Software Engineering & Knowledge Engineering, pp.92-97, 2004.
25 G. Salton and M. J. McGill, "Introduction to modern information retrieval," 1st ed., McGraw-Hill, 1983.