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Software companies spend over 45 percent of cost in dealing with softwarebugs. An inevitable step of fixing bugs is bug triage, which aims to correctlyassign a developer to a new bug. To decrease the time cost in manual work, textclassification techniques are applied to conduct automatic bug triage. In thispaper, we address the problem of data reduction for bug triage, i.e., how toreduce the scale and improve the quality of bug data. We combine instanceselection with feature selection to simultaneously reduce data scale on the bugdimension and the word dimension. To determine the order of applying instanceselection and feature selection, we extract attributes from historical bug datasets and build a predictive model for a new bug data set. We empiricallyinvestigate the performance of data reduction on totally 600,000 bug reports oftwo large open source projects, namely Eclipse and Mozilla. The results showthat our data reduction can effectively reduce the data scale and improve theaccuracy of bug triage. Our work provides an approach to leveraging techniqueson data processing to form reduced and high-quality bug data in softwaredevelopment and maintenance.

Towards Effective Bug Triage with Towards Effective Bug Triage with Software Data Reduction Techniques