Automated Classification of Software Bug Reports

Abstract

We target the problem of software bug reports classification. Our main aim is to build a classifier that is capable of classifying newly incoming bug reports into two predefined classes: corrective (defect fixing) report and perfective (major maintenance) report. This helps maintainers to quickly understand these bug reports and hence, allocate resources for each category. For this purpose, we propose a distinctive feature set that is based on the occurrences of certain keywords. The proposed feature set is then fed into a number of classification algorithms for building a classification model. The results of the proposed feature set achieved high accuracy in classification with SVM classification algorithm reporting an average accuracy of (93.1%) on three different open source projects.