An Automated Approach for Software Bug Classification.pdf

Abstract

Open source projects for example eclipse and fire fox have open source bug repositories. User reports bugs to these repositories. Users of these repositories are usually non-technical and cannot assign correct class to these bugs. Triaging of bugs to developer to fix them is a tedious and time consuming task. Developers are usually expert in some particular area. For example few developers are expert in GUI and others are in pure java functionality. Assigning a particular bug to relevant developer could save time as well as would help to maintain the interest level of developers by assigning those bugs according to their interest. However Assigning right bug to right developer is quite difficult for tri-ager without knowing the actual class a bug belongs to. In this research, I have classified the bugs in different labels on the basis of summary of the bug. I have used multinomial Naïve Bayes text classifier for Classification purpose. For feature selection Chi-Square and TFIDF algorithms were used. Using Naïve Bayes and chi- square we get average of 83 % accuracy.