Context

In GitHub, contributors make code changes, then create and submit pull requests to projects. Tags are a simple and effective way to attach additional information to pull requests and facilitate their organization. However, little effort has been devoted to study pull requests’ tags in GitHub.

Objective

Our objective in this paper is to propose an approach which automatically recommends tags for pull requests in GitHub.

Method

We make a survey on the usage of tags in pull requests. Survey results show that tags are useful for developers to track, search or classify pull requests. But some respondents think that it is difficult to choose right tags and keep consistency of tags. 60.61% of respondents think that a tag recommendation tool is useful. In order to help developers choose tags, we propose a method FNNRec which uses feed-forward neural network to analyze titles, description, file paths and contributors.

Results

We evaluate the effectiveness of FNNRec on 10 projects containing 68,497 tagged pull requests. The experimental results show that on average, FNNRec outperforms approach TagDeepRec and TagMulRec by 62.985% and 24.953% in terms of $F1-score@3,$ respectively.

Conclusion

FNNRec is useful to find appropriate tags and improve tag setting process in GitHub.

中文翻译：

---

推荐GitHub中请求请求的标签

语境

在GitHub中，贡献者进行代码更改，然后创建并将请求提交给项目。标签是一种简单有效的方法，可以附加其他信息以拉取请求并简化其组织。但是，在GitHub上很少花精力研究请求请求的标签。

目的

本文的目的是提出一种自动为GitHub中的请求请求推荐标签的方法。

方法

我们对拉取请求中标签的使用情况进行了调查。调查结果表明，标签对于开发人员跟踪，搜索或分类拉取请求很有用。但是一些受访者认为很难选择正确的标签并保持标签的一致性。60.61％的受访者认为标签推荐工具很有用。为了帮助开发人员选择标签，我们提出了一种FNNRec方法，该方法使用前馈神经网络来分析标题，描述，文件路径和贡献者。

结果

我们评估了FNNRec在包含68,497个带标签的请求请求的10个项目上的有效性。实验结果表明，FNNRec的平均性能比TagDeepRec和TagMulRec分别高62.985％和24.953％$F\mathrm{1个}-sCØ\mathrm{[R}Ë@3，$ 分别。

结论

FNNRec对于在GitHub中查找合适的标签并改善标签设置过程很有用。