  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.
