Abstract
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.
Abstract (translated)
大型开源项目会迅速收到大量问题报告（通常称为“错误”），这些报告包括用户和开发者社区提交的软件缺陷报告和新功能请求。由于项目的资源有限，无法处理所有的问题，因此需要根据项目的优先级以及问题本身的严重程度来进行优先排序。 本文提出了一种基于自然语言文本的新颖自动错误优先级确定方法。这种方法利用了存储在项目问题跟踪系统公开错误库中的bug报告的自然语言文本信息，并采用LDA的一种变体（称为TopicMiner-MTM）进行主题建模，同时使用BERT大型语言模型进行文本分类，在性能上超越了现有技术。 通过现有的参考数据集（包含Eclipse平台项目的85,156个错误报告）进行实验后发现，我们的方法在错误报告优先级预测的准确性、精确性、召回率和F1度量方面均优于现有的方法。
URL
https://arxiv.org/abs/2504.15912
