The Invisible Hand of AI Libraries Shaping Open Source Projects and Communities
Matteo Esposito, Andrea Janes, Valentina Lenarduzzi, Davide Taibi
TL;DR
The paper addresses how AI libraries are adopted in Open Source Software projects written in Python and Java and investigates their effects on development activity, community engagement, and code maintainability. It proposes a large scale SBOM driven study over $157.7k$ GitHub repositories to compare AI adopting versus non adopting projects, with three research questions and a detailed statistical analysis plan. The study design includes AI library selection from PyPI and Maven Central, SBOM parsing, and code metric analysis using Understand, aiming to produce actionable insights for researchers and practitioners. The work seeks to illuminate the socio technical dynamics of AI enabled OSS and provide a foundation for future investigations into AI driven software ecosystems.
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.