Abstract
In the bug triage we have an unavoidable step of fixing the bugs which helps in correctly assigning a developer to a new bug. Text classification and binary classification techniques are applied to decrease the time cost in manual work and to enhance the working of automatic bug triage. We address the problem of data reduction and hence we combine the instance selection and the feature selection algorithms to simultaneously reduce the data scale and enhance the accuracy of the bug reports in the bug triage. We determine a predictive model to perform the algorithms which adds on to prioritize the developer to a new bug by extracting attributes and the bug data set from the historical table. By leveraging data mining techniques, mining software repositories can uncover interesting information in software repositories and solve realworld software problem like Eclipse, Mozilla and GNOME.
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