基于嵌入模型的混合式相关缺陷关联方法

社交化编程使得开源社区中的知识可以快速被传播，其中，缺陷报告作为一类重要的软件开发知识，会含有特定的语义信息.通常，开发者会人工地将相关的缺陷报告关联起来.在一个软件项目中，发现并关联相关的缺陷报告可以为开发者提供更多的资源和信息去解决目标缺陷，从而提高缺陷修复效率.然而，现有人工关联缺陷报告的方法是十分耗费时间的，它在很大程度上取决于开发者自身的经验和知识.因此，研究如何及时、高效地关联相关缺陷是对于提高软件开发效率十分有意义的工作.将这类关联相关缺陷的问题视为推荐问题，并提出了一种基于嵌入模型的混合式相关缺陷关联方法，将传统的信息检索技术（TF-IDF）与深度学习中的嵌入模型（词嵌入模型和文档嵌入模型）结合起来.实验结果表明，该方法能够有效地提高传统方法的性能，且具有较强的应用扩展性.

Hybrid Approach for Linking Related Issues Based on Embedding Models

Social coding facilitates the sharing of knowledge in Open-source community. In particular, issue reports, as an important knowledge in the software development, usually contain relevant information, and can thus be linked to other related issues manually. In a project, identifying and linking issues to potentially related issues would provide developers more targeted resource and information when they resolve target issues, thus improving the issue resolution efficiency. However, the current manual linking approach is in general time-consuming and mainly depends on the experience and knowledge of the individual developers. Therefore, investigating how to link related issues timely is a meaningful task which can improve development efficiency of open-source projects. In this study, the problem of linking related issues is formulated as a recommendation problem and a hybrid approach based on embedding models is proposed, combining the traditional information retrieval technique, i.e., TF-IDF, and the embedding models in deep learning techniques, i.e., word embedding and document embedding. The evaluation results show that, the proposed approach can improve the performance of traditional approaches, with a very strong application scalability.