The Impact of Funding and Governance on Open-Source Development Activity

Open-source software (OSS) projects are becoming more prominent, with new funding and governance models emerging. However, the affinity between these models and project development activity is poorly understood. This study looks into the potential relationship between funding/governance systems and GitHub development metrics like commit and watch counts in OSS projects. A dataset of 660 OSS projects was evaluated to provide data on funding strategies (e.g., public token sales, crowdfunding, product sales), governance structures (e.g., decentralized autonomous organizations (DAOs), centralized foundations, private firms), and GitHub activity metrics. Project-level metrics were obtained by querying GitHub Archive data in BigQuery using Structured Query Language (SQL). At the same time, project websites and GitHub repositories were examined to classify funding and governance models and identify project types (e.g., cryptocurrency wallet, decentralized application (DApp), Layer 2 network). By comparing GitHub metrics across different funding and governance categories, this study aims to identify patterns and trends in development activity. The findings will help to further the understanding of the elements that determine OSS project performance, such as the impact of financial resources from public token sales versus traditional product sales on developer activity. Furthermore, this study will demonstrate how governance structures, such as DAOs, which promote community-driven development, differ from centralized private firms in terms of 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.