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论文类型：期刊论文

发表时间：2015-01-01

发表刊物：IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

收录刊物：SCIE、EI、Scopus

卷号：27

期号：1

页面范围：264-280

ISSN号：1041-4347

关键字：Mining software repositories; application of data preprocessing; data management in bug repositories; bug data reduction; feature selection; instance selection; bug triage; prediction for reduction orders

摘要：Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the problem of data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance.