在 GitHub 讨论中寻找相关帖子
Marcia Lima1,2, Igor Steinmacher3, Denae Ford4
1Department of Computer Science, Amazonas State University (UEA), Manaus, Amazonas, Brazil.
PeerJ. Computer science
|December 11, 2023
概括
本研究介绍了RD-Detector,这是一种使用Sentence-BERT的工具,可以在GitHub讨论中找到重复或类似的帖子. 这有助于减少噪音,并改善软件开发社区的知识共享.
科学领域:
- 软件工程 软件工程 软件工程
- 人与计算机的交互
- 自然语言处理自然语言处理.
背景情况:
- 软件开发依赖于协作工具,像GitHub Discussions这样的论坛越来越突出.
- 这些平台上的相关帖子 (重复,近重复) 制造噪音,妨碍知识共享.
研究的目的:
- 为了应对在GitHub讨论中检测相关帖子的挑战.
- 提出和评估一种自动化方法来识别重复和几乎重复的内容.
主要方法:
- 开发了RD-Detector,一种利用Sentence-BERT预训练模型的方法.
- 在3个GitHub社区的16048个讨论帖子中评估了RD-Detector.
- 软件维护人员和研究人员手动评估.
主要成果:
- 在检测相关帖子时,RD-Detector实现了77-100%的精度和66%的回忆.
- 手动验证证实了该方法的有效性.
- 维护者确定了合并讨论和整合信息的实际用途.
结论:
- RD-Detector提供了一种有效的解决方案,用于识别GitHub讨论中的相关帖子.
- 该工具可以显著减少维护人员所需的手工工作.
- 更好地检测相关帖子可以提高知识共享和平台的可用性.
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