The Invisible Hand of AI Libraries Shaping Open Source Projects and Communities

2026-01-05

arXiv.org

AI Summary

This study addresses the unclear adoption of artificial intelligence (AI) libraries in open-source software and their impact on development practices and community engagement. It presents the first systematic analysis of 157.7k Python and Java open-source repositories, comparing projects that adopt AI libraries with those that do not across dimensions such as development activity, community participation, and code complexity. Leveraging repository metadata and software metrics, the research employs a large-scale empirical methodology to uncover the technical and socio-organizational effects of AI library integration within the open-source ecosystem. By bridging a critical gap at the intersection of AI and open-source software engineering, this work provides foundational empirical evidence to inform the design and governance of AI-driven software development practices.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Philosophy and Ethics of AI: Philosophical Foundations of AI
• Intelligent Robots: Embodied AI

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In the early 1980s, Open Source Software emerged as a revolutionary concept amidst the dominance of proprietary software. What began as a revolutionary idea has now become the cornerstone of computer science. Amidst OSS projects, AI is increasing its presence and relevance. However, despite the growing popularity of AI, its adoption and impacts on OSS projects remain underexplored. We aim to assess the adoption of AI libraries in Python and Java OSS projects and examine how they shape development, including the technical ecosystem and community engagement. To this end, we will perform a large-scale analysis on 157.7k potential OSS repositories, employing repository metrics and software metrics to compare projects adopting AI libraries against those that do not. We expect to identify measurable differences in development activity, community engagement, and code complexity between OSS projects that adopt AI libraries and those that do not, offering evidence-based insights into how AI integration reshapes software development practices.

Problem

• AI libraries
• Open Source Software
• community engagement
• software development
• adoption

Innovation

• AI libraries
• open source software
• empirical study
• software metrics
• community engagement