Analyzing of Funding and Governance Structures on GitHub Development Activities in Open Source Software Projects

Open-source software (OSS) projects are increasingly gaining prominence with the emergence of new funding and governance models. However, the relationship between these models and project development activity remains unclear. This study examines the possible connection between funding/governance systems and development metrics on GitHub, such as commit and watch counts, in OSS projects. The first part of the study examines data from approximately 600 OSS projects using BigQuery, encompassing repository names, dates, actor IDs, actor logins, and 28 different events, including total activities and distinct commits, from 2013 to the present and information on funding mechanisms (such as public token sales, crowdfunding, and product sales) and governance structures (like decentralized autonomous organizations (DAOs), centralized foundations, and private companies) alongside GitHub activity metrics. Data from GitHub Archive was retrieved using SQL in BigQuery to obtain project-level metrics, while project websites and GitHub repositories were reviewed to categorize funding and governance models and identify project types (such as cryptocurrency wallets, decentralized applications (DApps), and Layer 2 networks). By comparing GitHub metrics across different funding and governance categories, this study aims to identify patterns and trends in development activity. The findings will provide deeper insights into factors influencing OSS project success, such as the impact of financial resources from public token sales versus traditional product sales on developer activity. Additionally, this research will demonstrate how governance structures, such as community-driven DAOs versus centralized private companies, can affect development workflows and project longevity. This abstract is part of a collection in which the overarching large project under Dr. Jiasun Li was subdivided into discrete critical tasks that were carried out by multiple individuals or smaller teams. Abstracts in this collection read similarly given the shared project goals, but represent distinct tasks completed by the abstract authors towards finalizing the described analysis.