用自然语言分析自动给开源项目漏洞报告排序，提升优先级判断准确率。
Automated Bug Report Prioritization in Large Open-Source Projects
- 结合主题建模与BERT模型分析漏洞报告文本
- 在Eclipse数据集上各项指标超越现有方法
- 适合需要高效处理海量漏洞报告的开发团队
大型开源项目每天收到大量问题，包括漏洞报告和功能请求，但资源有限，需优先处理。本文提出一种基于漏洞报告自然语言文本的自动化优先级排序方法。采用改进版LDA模型TopicMiner-MTM进行主题建模，并结合BERT大语言模型进行文本分类，显著提升预测性能。在包含85,156条漏洞报告的Eclipse平台参考数据集上的实验表明，该方法在准确率、精确率、召回率和F1值上均优于现有技术。
原文摘要 · Abstract (English)
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.
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