# Identify High-Impact Bug Reports by Combining the Data Reduction and Imbalanced Learning Strategies
## 2 Background Knowledge and Motivation  
In our work, we propose a high-impact bug report identification approach that combines data reduction, i.e., Data Reduction based on the Genetic algorithm (DRG), and imbalanced learning strategies. Thus, in this section, we introduce some background knowledge concerning bug report management. Moreover, we present the motivation of our study. Bug reports are valuable to software maintenance activities [23; 24; 25; 26]. Automatic support for bug report classification can facilitate understanding, resource allocation, and planning. [PERSON] et al. applied text mining techniques to the description of bug reports to determine whether a bug report is a real bug or a feature request [27]. They use techniques such as decision trees and logistic regression as well as the Naive Bayes classifier to achieve this purpose. [PERSON] et al. use rule learning techniques to predict the severity of bug reports [28]. Their approach was applied to five projects supplied by NASA's Independent Verification and Validation Facility. [PERSON] et al. [29] use the overall framework of machine learning to predict the bug report priority by considering factors such as time, text, author, related reports, severity, and product. [PERSON] et al. [30] established a predictive model to identify high-quality bug reports. The method effectively distinguishes between high-quality bug reports and low-quality bug reports based on extracting relevant descriptive information about the bug reports, products, operating systems, and bug report submitters. [PERSON] et al. [31] proposed a detection method for redundant bug reports based on information retrieval technology. This method treats each bug report as a document and obtains bug reports similar to the current bug report by calculating the similarity between the current bug report and the existing bug report. [PERSON] et al. [32] proposed a feature-based partitioning model that identified the similarities between bug reports. Subsequently, [PERSON] et al. [33] proposed a retrieval model based on multifeature information, which can continue to match the most similar features in bug reports. [PERSON] et al. [34] found that for approximately 80% of bug reports, the values of one or more of their fields (including the severity and fixer fields) are reassigned. They also show that redistributing field values for bug reports with existing field values consumes more time and cost than bug reports for no field reassignment. To resolve this problem, it is necessary to develop automatic approaches to perform severity predictions and semiautomatic fixer recommendations. [PERSON] [35] proposed a new approach for severity predictions and semiautomatic fixer recommendations to take the place of manual work by developers. In this approach, the top k nearest neighbors of a new bug report are extracted by computing a similarity measure called REP topic, which is an enhanced version of REP. Next, the severity prediction and semiautomatic fixer recommendation approaches are implemented based on the characteristics of these k neighbors, such as participating developers and textual similarities with the given bug report. [PERSON] et al. [36] applied test report prioritization methods to crowdsourced testing. They designed dynamic strategies to select the most dangerous and diverse test reports for inspection in each iteration. In their subsequent article, [PERSON] et al. [37] proposed a new technology to prioritize test reports for inspection by software developers. This approach combined image-understanding techniques with traditional text-based techniques, especially for the crowdsourced testing of mobile applications. They proposed prioritization approaches that are based on text descriptions, screenshot images, and a combination of both sources of information. Under the premise of obtaining rich training data, [PERSON] et al. [38] proposed a cluster-based classification approach for crowdsourcing report classification. However, sufficient training data are often not available.