Abstract:
A bug report is an important document that outlines
software problems that result in unexpected errors or wrong outcomes. In large software projects, a high number of bugs are
reported daily, which needs to be systematically analyzed. Predicting the priority level of reported bugs, assigning an appropriate
developer, finding duplicate issues, and predicting bug resolving
time are some of the critical tasks in the bug analysis process. Due
to the inherent complexity of the bug analyzing process, manual bug
investigation requires a significant amount of time, resources, and
effort. Therefore, the need to establish automated or semi-automated
approaches for assessing bug reports is extensively discussed in the
literature. This research presents a novel approach to prioritize the
bug reports by exploiting a Convolutional Neural Network-based
approach. Furthermore, this research investigates the impact of both
textual and categorical features of bug reports in improving the
accuracy of priority prediction. The experiments were conducted
by extracting the bug reports available in three GitHub repositories.
The evaluation results confirm that the use of categorical features
does not have an impact on the accuracy of the priority prediction
of bug reports. Furthermore, it was observed that better prediction
accuracies are shown for the datasets extracted from Bugzilla than
GitHub repository
