Understanding Bot Adoption on the Platform GitHub: A Project Level Analysis of Open-Source Software Communities
Category: Research Poster
Author(s): Kacy Baumgart
Presenter(s): Kacy Baumgart
Mentors(s): Zisu Wang
Open-source software (OSS) development increasingly relies on automation tools, particularly bots, to manage the growing complexity of collaborative projects. While prior research has examined bot usage across various contribution workflows, little is known about what drives project-level decisions to adopt bots and how those decisions vary across different types of OSS projects. This study analyzes the circumstances under which OSS projects adopt bots in GitHub issues, posts used by users to report bugs, request features, and coordinate project tasks. It investigates the question: under what conditions do projects integrate automation into their issue management workflows? Using a dataset of GitHub repositories, this research identifies which projects use bots in issues and characterizes the contextual factors surrounding adoption. We examine project-level attributes, including project size, number of contributors, and adoption trends over time, to determine what distinguishes projects that adopt bots from those that do not. We situate our analysis within the broader literature on OSS automation to understand how issue management practices evolve as projects grow. This study can help open-source developers make more informed decisions about when to adopt bots for issue management, and guide platform designers in building better automation tools for managing project workflows. This work also advances our understanding of how OSS communities formalize and scale their coordination structures as projects grow in complexity. It contributes to a richer, more contextual understanding of automation in OSS communities, helping to explain not just whether projects use bots in issues, but under what circumstances that choice is made.