# Identify High-Impact Bug Reports by Combining the Data Reduction and Imbalanced Learning Strategies
## 1 Introduction
[PERSON] [12] investigated four widely used imbalanced learning strategies (i.e., random under-sampling (RUS), random over-sampling (ROS), synthetic minority over-sampling technique (SMOTE) and cost-matrix adjuster (CMA)) to solve the class imbalance distribution of bug reports from four different open source projects. However, the bug reports are written in natural language, which includes much noise. Therefore, the internal methods (CMA) are unsuitable to balance the distribution of bug reports [13]. In addition, RUS could cause under-fitting, whereas ROS could cause over-fitting [14; 15; 16; 17]. Additionally, the SMOTE is based on low-dimensional random sampling, which has a poor generalization ability [18; 19]. Furthermore, random sampling (RUS, ROS, SMOTE) could bring a certain degree of uncertainty, and some sampling results are not in accord with the real distribution of the dataset. In this paper, we propose an approach for identifying high-impact bug reports by combining data reduction, named Data Reduction based on Genetic algorithm (DRG), with imbalanced learning strategies. In data reduction phase, we use four feature selection algorithms (FS) (i.e., one rule (OneR), information gain (IG), chi-square (CHI), and filtered selection (Relief) (Described in Appendix A.2)) to extract 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of the attributes from bug reports with reduced bug data dimensions and word dimensions to solve the low-quality bug reports challenge and obtain 36 feature selection schemes, i.e., feature reduction (GA(FS)), and use instance selection (IS) algorithms (i.e., condensed nearest neighbor (CNN), edited nearest neighbor (ENN), minimal consistent set (MCS), and iterative case filter (ICF)) to randomly generate 36 instance selection schemes, i.e., instance reduction (GA(IS)). Simultaneous feature and instance reduction (i.e., GA (FS+IS)) is a combined extraction scheme of 36 features and instance strings; imbalanced learning strategies are applied to the dataset after the reduction operation. In the imbalanced learning strategies phase, we investigate four widely used imbalanced learning strategies, i.e., RUS, ROS, SMOTE and CMA, and four popular classfication algorithms, i.e., naive Bayes (NB), naive Bayes multinomial (NBM), support vector machine (SVM) and K-nearest neighbors (KNN) (Described in Appendix A.1), and combine them to make a total of 16 different combinations (i.e., variants) to identify high-impact bug reports for reduced datasets. Comprehensive experiments have been conducted on public datasets obtained from real-world bug repositories (Mozilla [20], GCC [21] and Eclipse [22]). In addition, we also verify the performance of our method in the case of different combinations of FS and IS, data reduction and imbalanced learning strategies. The main contributions of this paper are as follows:  
* We present a data reduction method based on the genetic algorithm to reduce the data for identifying high-impact bug reports. This problem aims to augment the data set by identifying two aspects of the high-impact bug reports, namely, (a) to simultaneously reduce the scales of the bug report dimension and the word dimension and (b) to improve the ability to identifying high-impact bug reports. * We verify the ability of our method to identify the high-impact bug reports in the case of different combinations of FS and IS, data reduction and imbalanced learning strategies. * Five evaluation criteria are used in experimental part to evaluate the proposed method. The results from three public bug repository datasets (Mozilla, GCC and Eclipse) show that our method could effectively identify the high-impact bug reports. The remainder of this paper is organized as follows: the related studies and the motivation of our approach are discussed in Section 2, the design of our approach is discussed in Section 3, the experimental design and results are presented in Sections 4 and 5, and the conclusions are discussed in Section 6.