# Identifying Unmaintained Projects in GitHub

arXiv: https://arxiv.org/abs/1809.04041 · HTML: https://mlsanity.com/paper/1809.04041/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [Automatic Analysis of Available Source Code of Top Artificial Intelligence Conference Papers](https://mlsanity.com/paper/2209.14155/) (2022)
- [A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions](https://mlsanity.com/paper/2103.03610/) (2021)
- [Human-AI Synergy in Agentic Code Review](https://mlsanity.com/paper/2603.15911/) (2026)
- [Detecting Security Patches via Behavioral Data in Code Repositories](https://mlsanity.com/paper/2302.02112/) (2023)
- [From Human-Centric to Agentic Code Review: The Impact of Different Generations of Generative AI Technology on Review Quality](https://mlsanity.com/paper/2607.13196/) (2026)

## Abstract (arXiv)

Background: Open source software has an increasing importance in modern software development. However, there is also a growing concern on the sustainability of such projects, which are usually managed by a small number of developers, frequently working as volunteers. Aims: In this paper, we propose an approach to identify GitHub projects that are not actively maintained. Our goal is to alert users about the risks of using these projects and possibly motivate other developers to assume the maintenance of the projects. Method: We train machine learning models to identify unmaintained or sparsely maintained projects, based on a set of features about project activity (commits, forks, issues, etc). We empirically validate the model with the best performance with the principal developers of 129 GitHub projects. Results: The proposed machine learning approach has a precision of 80%, based on the feedback of real open source developers; and a recall of 96%. We also show that our approach can be used to assess the risks of projects becoming unmaintained. Conclusions: The model proposed in this paper can be used by open source users and developers to identify GitHub projects that are not actively maintained anymore.