Bug reduction software data technology





                                                  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 developer to a new bug.  To decrease the time cost in manual work, classificatiotechniquesare applied to conduct automatic bug triage. In this paper, we address the problem of dareduction 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 frhistorical bug data sets and build a predictive model for a new bug data set.We empirically investigat the performance of data reduction on totally 600,000 bug reports of two large open soprojects, 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 tleveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance.



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