# How do Machine Learning Projects use Continuous Integration Practices? An Empirical Study on GitHub Actions

arXiv: https://arxiv.org/abs/2403.09547 · HTML: https://mlsanity.com/paper/2403.09547/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Studying Software Engineering Patterns for Designing Machine Learning Systems](https://mlsanity.com/paper/1910.04736/) (2019)
- [How Do Software Developers Use GitHub Actions to Automate Their Workflows?](https://mlsanity.com/paper/2103.12224/) (2021)
- [The Effects of Continuous Integration on Software Development: a Systematic Literature Review](https://mlsanity.com/paper/2103.05451/) (2021)
- [The Impact of a Continuous Integration Service on the Delivery Time of Merged Pull Requests](https://mlsanity.com/paper/2305.16365/) (2023)
- [Continuous Integration Theater](https://mlsanity.com/paper/1907.01602/) (2019)
- [Investigating the Impact of Continuous Integration Practices on the Productivity and Quality of Open-Source Projects](https://mlsanity.com/paper/2208.02598/) (2022)

## Abstract (arXiv)

Continuous Integration (CI) is a well-established practice in traditional software development, but its nuances in the domain of Machine Learning (ML) projects remain relatively unexplored. Given the distinctive nature of ML development, understanding how CI practices are adopted in this context is crucial for tailoring effective approaches. In this study, we conduct a comprehensive analysis of 185 open-source projects on GitHub (93 ML and 92 non-ML projects). Our investigation comprises both quantitative and qualitative dimensions, aiming to uncover differences in CI adoption between ML and non-ML projects. Our findings indicate that ML projects often require longer build durations, and medium-sized ML projects exhibit lower test coverage compared to non-ML projects. Moreover, small and medium-sized ML projects show a higher prevalence of increasing build duration trends compared to their non-ML counterparts. Additionally, our qualitative analysis illuminates the discussions around CI in both ML and non-ML projects, encompassing themes like CI Build Execution and Status, CI Testing, and CI Infrastructure. These insights shed light on the unique challenges faced by ML projects in adopting CI practices effectively.