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

arXiv: https://arxiv.org/abs/2601.01944 · HTML: https://mlsanity.com/paper/2601.01944/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 71 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [In-IDE Code Generation from Natural Language: Promise and Challenges](https://mlsanity.com/paper/2101.11149/) (2021)
- [Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps](https://mlsanity.com/paper/1802.02312/) (2018)
- [What's in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform](https://mlsanity.com/paper/1811.07643/) (2018)
- [Do Developers Update Their Library Dependencies? An Empirical Study on the Impact of Security Advisories on Library Migration](https://mlsanity.com/paper/1709.04621/) (2017)
- [Sampling Projects in GitHub for MSR Studies](https://mlsanity.com/paper/2103.04682/) (2021)
- [Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study](https://mlsanity.com/paper/2402.16480/) (2024)
- [Generative AI for Software Architecture. Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2503.13310/) (2025)
- [An Extensive Comparison of Static Application Security Testing Tools](https://mlsanity.com/paper/2403.09219/) (2024)
- [Leveraging Large Language Models for Preliminary Security Risk Analysis: A Mission-Critical Case Study](https://mlsanity.com/paper/2403.15756/) (2024)
- [On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?](https://mlsanity.com/paper/2412.11698/) (2024)
- [Vulnerable Open Source Dependencies: Counting Those That Matter](https://mlsanity.com/paper/1808.09753/) (2018)
- [6GSoft: Software for Edge-to-Cloud Continuum](https://mlsanity.com/paper/2407.05963/) (2024)
- [Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries Across Software Package Ecosystems](https://mlsanity.com/paper/2201.07201/) (2022)
- [A Call for Critically Rethinking and Reforming Data Analysis in Empirical Software Engineering](https://mlsanity.com/paper/2501.12728/) (2025)
- [Characterizing the Usage, Evolution and Impact of Java Annotations in Practice](https://mlsanity.com/paper/1805.01965/) (2018)
- [CAM: A Collection of Snapshots of GitHub Java Repositories Together with Metrics](https://mlsanity.com/paper/2403.08488/) (2024)

## Abstract (arXiv)

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.