UC Irvine

Similar to social media platforms, people use emojis in software development-related com- munication to enrich the context and convey additional emotion. With the increasing emoji use in software development-related communication, it has become important to understand why software developers are using emojis and their impact. Gaining a deeper understanding is essential because the intention of emoji usage might be affected by the demographics and experience of developers; also, frequency and the distribution of emoji usage might change depending on the activity, stage of the development, and nature of the conversation, etc. In this paper, we present a large-scale empirical study on the intention of emoji usage conducted on 2,712 Open Source Software (OSS) projects. We build a machine learning model to auto- mate classifying the intentions behind emoji usage in 39,980 posts. We also study the impact of emoji usage on pull request acceptance and examine if emoji usages can help facilitate the speed of getting a response. To validate the outcomes, we surveyed 60 open-source software developers from 17 countries to understand developers’ perceptions of why and when emojis are used. Our results show that we can classify the intention of emoji usage with high accuracy (AUC of 0.97). In addition, the results indicate that developers use emoji for varying intentions, and emoji usage intention changes throughout a conversation. Our results also show that emoji usage has a significant association with quicker responses which leads us to posit that an emoji conveying a positive sentiment will speed up the response time than an emoji conveying a negative sentiment. However, our results does not show that emoji usage has an impact on pull request acceptance. Our study opens a new avenue in Software Engineering research related to automatically identifying the intention of the emoji use that can help improve the communication efficiency and help project maintainers monitor and ensure the quality of communication. Another thread of future research could look into what intentions of emoji usage or what kind of emojis are more likely to attract users and how that is associated with emoji usage diffusion in different levels (threads, projects, etc.)