Improved Approach for Predicting the Bug Triage Using Data Reduction Methods
نویسندگان
چکیده
Most of the software companies need to deal with vast number of software bugs day to day. This paper can be viewed as an application of instance selection and feature selection in bug repositories. The aim is to address the problem of data reduction for bug triage, and to reduce the scale and improve the quality of bug data. This can be achieved by combining instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. Data reduction can be addressed by combining instance selection and feature selection approaches.The reduced data set contains lesser bug data with fewer bug reports and fewer words than the original bug data and provide similar information over the original bug data and build a binary classifier to predict the order of applying instance selection and feature selection. Determine the order of applying instance selection and feature selection, to extract attributes from historical bug data sets and build a predictive model for a new bug data set. Empirically investigate the performance of data reduction on bug reports of two large open source projects, namely Eclipse and Mozilla.
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