Automated Bug Report Prioritization in Large Open-Source Projects
This addresses resource constraints in open-source software development by automating bug prioritization, but it is incremental as it builds on existing methods like LDA and BERT.
The paper tackles the problem of prioritizing bug reports in large open-source projects by proposing an automated approach using natural language text, achieving higher performance in accuracy, precision, recall, and F1-measure compared to state-of-the-art methods on a dataset of 85,156 bug reports.
Large open-source projects receive a large number of issues (known as bugs), including software defect (i.e., bug) reports and new feature requests from their user and developer communities at a fast rate. The often limited project resources do not allow them to deal with all issues. Instead, they have to prioritize them according to the project's priorities and the issues' severities. In this paper, we propose a novel approach to automated bug prioritization based on the natural language text of the bug reports that are stored in the open bug repositories of the issue-tracking systems. We conduct topic modeling using a variant of LDA called TopicMiner-MTM and text classification with the BERT large language model to achieve a higher performance level compared to the state-of-the-art. Experimental results using an existing reference dataset containing 85,156 bug reports of the Eclipse Platform project indicate that we outperform existing approaches in terms of Accuracy, Precision, Recall, and F1-measure of the bug report priority prediction.