# Does Code Quality Affect Pull Request Acceptance? An empirical study

arXiv: https://arxiv.org/abs/1908.09321 · HTML: https://mlsanity.com/paper/1908.09321/

## Key facts

- Cited by 9 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 25 Aug 2019.

## Built on by (most cited first, 9 of 9)

- [Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub](https://mlsanity.com/paper/2601.15195/) (2026)
- [On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests](https://mlsanity.com/paper/2110.15447/) (2021)
- [Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub](https://mlsanity.com/paper/2601.00477/) (2026)
- [Follow Your Nose -- Which Code Smells are Worth Chasing?](https://mlsanity.com/paper/2103.01861/) (2021)
- [Exploring the Challenges and Opportunities of AI-assisted Codebase Generation](https://mlsanity.com/paper/2508.07966/) (2025)
- [Predicting the First Response Latency of Maintainers and Contributors in Pull Requests](https://mlsanity.com/paper/2311.07786/) (2023)
- [Understanding the Helpfulness of Stale Bot for Pull-based Development: An Empirical Study of 20 Large Open-Source Projects](https://mlsanity.com/paper/2305.18150/) (2023)
- [Which Alert Removals are Beneficial?](https://mlsanity.com/paper/2603.21322/) (2026)
- [How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests](https://mlsanity.com/paper/2607.21832/) (2026)

## Abstract (arXiv)

Background. Pull requests are a common practice for contributing and reviewing contributions, and are employed both in open-source and industrial contexts. One of the main goals of code reviews is to find defects in the code, allowing project maintainers to easily integrate external contributions into a project and discuss the code contributions. Objective. The goal of this paper is to understand whether code quality is actually considered when pull requests are accepted. Specifically, we aim at understanding whether code quality issues such as code smells, antipatterns, and coding style violations in the pull request code affect the chance of its acceptance when reviewed by a maintainer of the project. Method. We conducted a case study among 28 Java open-source projects, analyzing the presence of 4.7 M code quality issues in 36 K pull requests. We analyzed further correlations by applying Logistic Regression and seven machine learning techniques (Decision Tree, Random Forest, Extremely Randomized Trees, AdaBoost, Gradient Boosting, XGBoost). Results. Unexpectedly, code quality turned out not to affect the acceptance of a pull request at all. As suggested by other works, other factors such as the reputation of the maintainer and the importance of the feature delivered might be more important than code quality in terms of pull request acceptance. Conclusions. Researchers already investigated the influence of the developers' reputation and the pull request acceptance. This is the first work investigating if quality of the code in pull requests affects the acceptance of the pull request or not. We recommend that researchers further investigate this topic to understand if different measures or different tools could provide some useful measures.