A First Look at Developers' Live Chat on Gitter

TL;DR

This paper provides a comprehensive empirical analysis of developers' live chat interactions on Gitter, revealing patterns in timing, community structures, discussion topics, and interaction behaviors to enhance understanding of OSS communication.

Contribution

It is the first large-scale empirical study analyzing developers' live chat data, offering insights into communication patterns and community structures in open source software development.

Findings

Developers chat more on weekdays, especially Wednesdays and Thursdays.

Three common community structures are identified.

Six interaction patterns in live chat communities are discovered.

Abstract

Modern communication platforms such as Gitter and Slack play an increasingly critical role in supporting software teamwork, especially in open source development.Conversations on such platforms often contain intensive, valuable information that may be used for better understanding OSS developer communication and collaboration. However, little work has been done in this regard. To bridge the gap, this paper reports a first comprehensive empirical study on developers' live chat, investigating when they interact, what community structures look like, which topics are discussed, and how they interact. We manually analyze 749 dialogs in the first phase, followed by an automated analysis of over 173K dialogs in the second phase. We find that developers tend to converse more often on weekdays, especially on Wednesdays and Thursdays (UTC), that there are three common community structures observed, that developers tend to discuss topics such as API usages and errors, and that six dialog interaction patterns are identified in the live chat communities. Based on the findings, we provide recommendations for individual developers and OSS communities, highlight desired features for platform vendors, and shed light on future research directions. We believe that the findings and insights will enable a better understanding of developers' live chat, pave the way for other researchers, as well as a better utilization and mining of knowledge embedded in the massive chat history.