# An Insight into the Pull Requests of GitHub

arXiv: https://arxiv.org/abs/1807.01853 · HTML: https://mlsanity.com/paper/1807.01853/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [Towards a Change Taxonomy for Machine Learning Systems](https://mlsanity.com/paper/2203.11365/) (2022)
- [Exploring the Challenges and Opportunities of AI-assisted Codebase Generation](https://mlsanity.com/paper/2508.07966/) (2025)
- [What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects](https://mlsanity.com/paper/2509.07763/) (2025)
- [Automatic Pull Request Description Generation Using LLMs: A T5 Model Approach](https://mlsanity.com/paper/2408.00921/) (2024)

## Abstract (arXiv)

Given the increasing number of unsuccessful pull requests in GitHub projects, insights into the success and failure of these requests are essential for the developers. In this paper, we provide a comparative study between successful and unsuccessful pull requests made to 78 GitHub base projects by 20,142 developers from 103,192 forked projects. In the study, we analyze pull request discussion texts, project specific information (e.g., domain, maturity), and developer specific information (e.g., experience) in order to report useful insights, and use them to contrast between successful and unsuccessful pull requests. We believe our study will help developers overcome the issues with pull requests in GitHub, and project administrators with informed decision making.