Open source software development has become more social and collaborative, evident GitHub. Since 2016, GitHub started to support more informal methods such as emoji reactions, with the goal to reduce commenting noise when reviewing any code changes to a repository. From a code review context, the extent to which emoji reactions facilitate a more efficient review process is unknown. We conduct an empirical study to mine 1,850 active repositories across seven popular languages to analyze 365,811 Pull Requests (PRs) for their emoji reactions against the review time, first-time contributors, comment intentions, and the consistency of the sentiments. Answering these four research perspectives, we first find that the number of emoji reactions has a significant correlation with the review time. Second, our results show that a PR submitted by a first-time contributor is less likely to receive emoji reactions. Third, the results reveal that the comments with an intention of information giving, are more likely to receive an emoji reaction. Fourth, we observe that only a small proportion of sentiments are not consistent between comments and emoji reactions, i.e., with 11.8% of instances being identified. In these cases, the prevalent reason is when reviewers cheer up authors that admit to a mistake, i.e., acknowledge a mistake. Apart from reducing commenting noise, our work suggests that emoji reactions play a positive role in facilitating collaborative communication during the review process.

翻译：开源软件开发已变得更加社交化和协作化，这在 GitHub 上尤为明显。自 2016 年起，GitHub 开始支持表情符号反应等非正式方式，旨在减少审查仓库代码变更时的评论噪音。然而，在代码审查背景下，表情符号反应在多大程度上能促进更高效的审查流程仍属未知。我们开展了一项实证研究，挖掘了七种流行语言中的 1,850 个活跃仓库，分析了 365,811 个拉取请求的表情符号反应与审查时间、首次贡献者、评论意图及情感一致性之间的关系。针对这四个研究视角，我们首先发现，表情符号反应数量与审查时间存在显著相关性。其次，结果表明，首次贡献者提交的 PR 收到表情符号反应的可能性较低。第三，研究揭示，以信息提供为意图的评论更可能获得表情符号反应。第四，我们观察到仅少数评论与表情符号反应之间的情感不一致，即识别出 11.8% 的实例。在这些情况下，主要原因是审查者鼓励承认错误的作者（即承认错误）。除了减少评论噪音外，我们的研究还表明，表情符号反应在促进审查过程中的协作沟通方面发挥了积极作用。