? High-Impact Bug Report Identification with Imbalanced Learning Strategies
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (1) :181-198    DOI: 10.1007/s11390-017-1713-3
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High-Impact Bug Report Identification with Imbalanced Learning Strategies
Xin-Li Yang1(杨昕立), David Lo2, Member, ACM, IEEE, Xin Xia1,*(夏鑫), Member, CCF, ACM, IEEE, Qiao Huang1(黄乔), and Jian-Ling Sun1(孙建伶), Member, CCF, ACM
1 College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China;
2 School of Information Systems, Singapore Management University, Singapore, Singapore

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Abstract In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects (i.e., surprise bugs), or break pre-existing functionalities and destroy the user experience (i.e., breakage bugs). Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy feat. Thus, an automated technique that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. Considering that only a small proportion of bugs are high-impact bugs, the identification of high-impact bug reports is a difficult task. In this paper, we propose an approach to identify high-impact bug reports by leveraging imbalanced learning strategies. We investigate the effectiveness of various variants, each of which combines one particular imbalanced learning strategy and one particular classification algorithm. In particular, we choose four widely used strategies for dealing with imbalanced data and four state-of-the-art text classification algorithms to conduct experiments on four datasets from four different open source projects. We mainly perform an analytical study on two types of high-impact bugs, i.e., surprise bugs and breakage bugs. The results show that different variants have different performances, and the best performing variants SMOTE (synthetic minority over-sampling technique)+KNN (K-nearest neighbours) for surprise bug identification and RUS (random under-sampling)+NB (naive Bayes) for breakage bug identification outperform the F1-scores of the two state-of-the-art approaches by Thung et al. and Garcia and Shihab.
Articles by authors
Xin-Li Yang
David Lo
Xin Xia
Qiao Huang
Jian-Ling Sun
Keywordshigh-impact bug   imbalanced learning   bug report identification     
Received 2016-03-19;

A preliminary version of the paper was published in the Proceedings of COMPSAC 2016.This work is supported by the National Natural Science Foundation of China under Grant Nos. 61602403 and 61402406 and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant No. 2015BAH17F01.

Corresponding Authors: Xin Xia     Email: xxia@zju.edu.cn
About author: Xin Xia received his Ph.D. degree in computer science from the College of Computer Science and Technology, Zhejiang University, Hangzhou, in 2014. He is currently a research assistant professor in the College of Computer Science and Technology at Zhejiang University, Hangzhou. His research interests include software analytic, empirical study, and mining software repository.
Cite this article:   
Xin-Li Yang, David Lo, Xin Xia, Qiao Huang, Jian-Ling Sun.High-Impact Bug Report Identification with Imbalanced Learning Strategies[J]  Journal of Computer Science and Technology, 2017,V32(1): 181-198
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