A Bug or a Suggestion? An Automatic Way to Label Issues
This addresses the challenge of prioritizing bug identification in ITSs for developers and users, though it is incremental as it builds on existing classification methods.
The paper tackled the problem of automatically identifying bug reports across different Issue Tracking Systems (ITSs) by proposing a deep learning-based approach, which achieved an F-measure of 85.6% in distinguishing bugs from other issues, significantly outperforming benchmark methods.
More and more users and developers are using Issue Tracking Systems (ITSs) to report issues, including bugs, feature requests, enhancement suggestions, etc. Different information, however, is gathered from users when issues are reported on different ITSs, which presents considerable challenges for issue classification tools to work effectively across the ITSs. Besides, bugs often take higher priority when it comes to classifying the issues, while existing approaches to issue classification seldom focus on distinguishing bugs and the other non-bug issues, leading to suboptimal accuracy in bug identification. In this paper, we propose a deep learning-based approach to automatically identify bug-reporting issues across various ITSs. The approach implements the k-NN algorithm to detect and correct misclassifications in data extracted from the ITSs, and trains an attention-based bi-directional long short-term memory (ABLSTM) network using a dataset of over 1.2 million labelled issues to identify bug reports. Experimental evaluation shows that our approach achieved an F-measure of 85.6\% in distinguishing bugs and other issues, significantly outperforming the other benchmark and state-of-the-art approaches examined in the experiment.