Surprise Bug Report Prediction Utilizing Optimized Integration with Imbalanced Learning Strategy. (19th February 2020)

• Record Type: Journal Article
• Authors: Li, Hui; Qu, Yang; Guo, Shikai; Gao, Guofeng; Chen, Rong; Chen, Guo
• Other Names: Mobayen Saleh Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-02-19
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/8509821
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : In software projects, a large number of bugs are usually reported to bug repositories. Due to the limited budge and work force, the developers often may not have enough time and ability to inspect all the reported bugs, and thus they often focus on inspecting and repairing the highly impacting bugs. Among the high-impact bugs, surprise bugs are reported to be a fatal threat to the software systems, though they only account for a small proportion. Therefore, the identification of surprise bugs becomes an important work in practices. In recent years, some methods have been proposed by the researchers to identify surprise bugs. Unfortunately, the performance of these methods in identifying surprise bugs is still not satisfied for the software projects. The main reason is that surprise bugs only occupy a small percentage of all the bugs, and it is difficult to identify these surprise bugs from the imbalanced distribution. In order to overcome the imbalanced category distribution of the bugs, a method based on machine learning to predict surprise bugs is presented in this paper. This method takes into account the textual features of the bug reports and employs an imbalanced learning strategy to balance the datasets of the bug reports. Then these datasets after balancing are used to train three selected classifiers which are built by three different classification algorithms and predict the datasets with unknown type. In particular, an ensemble method named optimization integration is proposed to generate a unique and best result, according to the results produced by the three classifiers. This ensemble method is able to adjust the ability of the classifier to detect different categories based on the characteristics of different projects and integrate the advantages of three classifiers. The experiments performed on the datasets from 4 software projects show that this method performs better than the previous methods in terms of detecting surprise bugs.