Using Categorical Features in Mining Bug Tracking Systems to Assign Bug Reports

TL;DR

This paper explores the use of categorical features in bug reports to improve bug assignment accuracy, demonstrating that such features can enhance classification performance over traditional text-based methods.

Contribution

It introduces a classification approach leveraging categorical fields of bug reports, showing their effectiveness in improving bug assignment accuracy.

Findings

Categorical features can improve bug report classification accuracy.

Textual content remains important for bug assignment.

Using categorical data reduces computational complexity.

Abstract

Most bug assignment approaches utilize text classification and information retrieval techniques. These approaches use the textual contents of bug reports to build recommendation models. The textual contents of bug reports are usually of high dimension and noisy source of information. These approaches suffer from low accuracy and high computational needs. In this paper, we investigate whether using categorical fields of bug reports, such as component to which the bug belongs, are appropriate to represent bug reports instead of textual description. We build a classification model by utilizing the categorical features, as a representation, for the bug report. The experimental evaluation is conducted using three projects namely NetBeans, Freedesktop, and Firefox. We compared this approach with two machine learning based bug assignment approaches. The evaluation shows that using the textual contents of bug reports is important. In addition, it shows that the categorical features can improve the classification accuracy.