Combining Imbalance Learning Strategy and Multiclassifier Estimator for Bug Report Classification. (17th February 2020)

• Record Type: Journal Article
• Authors: Guo, Shikai; Wang, Siwen; Wei, Miaomiao; Chen, Rong; Guo, Chen; Li, Hui
• Other Names: Pan Weifeng Guest Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-02-17
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/5712461
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Since a large number of bug reports are submitted to the bug repository every day, efficiently assigning bug reports to the correct developer is a considerable challenge. Because of the large differences between the different components of different projects, the current bug classification mainly relies on the components of the bug report to dispatch bug reports to the designated developer or developer community. Unfortunately, the component information of the bug report is filled in by default according to the bug submitter and the result is often incorrect. Thus, an automatic technology 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. In this paper, we propose a method based on the combination of imbalanced learning strategies such as random undersampling (RUS), random oversampling (ROS), synthetic minority oversampling technique (SMOTE), and AdaCost algorithms with multiclass classification methods, OVO and OVA, to solve bug reports component classification problem. We investigate the effectiveness of different combinations, i.e., variants, each of which includes a specific imbalance learning strategy and a specific classification algorithm. We mainly perform an analytical study on five open bug repositories (Eclipse, Mozilla, GCC, OpenOffice, and NetBeans). The results show that different variants have different performance for bug reports component identification and the best performance variants are combined with the imbalanced learning strategy RUS and the OVA method based on the SVM classifier.