Social coding facilitates the sharing of knowledge in GitHub projects. In particular, issue reports, as an important knowledge in the software development, usually contain relevant information, and can thus be shared and linked in the developers’ discussion to aid the issue resolution. Linking issues to potentially related issues, i.e. issue knowledge acquisition, would provide developers with more targeted resource and information when they search and resolve issues. However, identifying and acquiring related issues is in general challenging, because the real-world acquiring practice is time-consuming and mainly depends on the experience and knowledge of the individual developers. Therefore, acquiring related issues automatically is a meaningful task which can improve development efficiency of GitHub projects. In this paper, we formulate the problem of acquiring related issue knowledge as a recommendation problem. To solve this problem, we propose a novel approach, iLinker, combining information retrieval technique, i.e. TF-IDF, and deep learning techniques, i.e. Word Embedding and Document Embedding. Our evaluation results show that, in both coarse-grained recommendation and fine-grained recommendation tasks, iLinker outperforms the baseline approaches.

中文翻译：

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iLinker：一种在GitHub项目中获取问题知识的新颖方法

社交编码有助于在GitHub项目中共享知识。特别是，问题报告作为软件开发中的重要知识，通常包含相关信息，因此可以在开发人员的讨论中共享和链接，以帮助解决问题。将问题链接到潜在的相关问题，即问题知识的获取，将为开发人员在搜索和解决问题时提供更多有针对性的资源和信息。但是，识别和获取相关问题通常具有挑战性，因为现实世界中的获取实践非常耗时，并且主要取决于各个开发人员的经验和知识。因此，自动获取相关问题是一项有意义的任务，可以提高GitHub项目的开发效率。在本文中，我们将获取相关问题知识的问题公式化为推荐问题。为了解决这个问题，我们提出了一种新颖的方法iLinker，它结合了信息检索技术（即TF-IDF）和深度学习技术（即，词嵌入和文档嵌入。我们的评估结果表明，在粗粒度推荐和细粒度推荐任务中，iLinker均优于基准方法。