# On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests

arXiv: https://arxiv.org/abs/2110.15447 · HTML: https://mlsanity.com/paper/2110.15447/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 88 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [Understanding the Factors that Impact the Popularity of GitHub Repositories](https://mlsanity.com/paper/1606.04984/) (2016)
- [What's in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform](https://mlsanity.com/paper/1811.07643/) (2018)
- [Does Code Quality Affect Pull Request Acceptance? An empirical study](https://mlsanity.com/paper/1908.09321/) (2019)
- [Pull Request Latency Explained: An Empirical Overview](https://mlsanity.com/paper/2108.09946/) (2021)
- [On the Relationship Between the Developer's Perceptible Race and Ethnicity and the Evaluation of Contributions in OSS](https://mlsanity.com/paper/2104.06143/) (2021)
- [How Successful Are Open Source Contributions From Countries with Different Levels of Human Development?](https://mlsanity.com/paper/2012.03716/) (2020)
- [Pull Request Decision Explained: An Empirical Overview](https://mlsanity.com/paper/2105.13970/) (2021)
- [GAP2WSS: A Genetic Algorithm based on the Pareto Principle for Web Service Selection](https://mlsanity.com/paper/2109.10430/) (2021)

## Built on by (most cited first, 6 of 6)

- [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)
- [Automatic Detection of LLM-Generated Code: A Comparative Case Study of Contemporary Models Across Function and Class Granularities](https://mlsanity.com/paper/2409.01382/) (2024)
- [Understanding the Rejection of Fixes Generated by Agentic Pull Requests -- Insights from the AIDev Dataset](https://mlsanity.com/paper/2606.13468/) (2026)
- [The Impact of Environment Configurations on the Stability of AI-Enabled Systems](https://mlsanity.com/paper/2408.02825/) (2024)
- [An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbots](https://mlsanity.com/paper/2410.07094/) (2024)

## Abstract (arXiv)

Pull-based development has enabled numerous volunteers to contribute to open-source projects with fewer barriers. Nevertheless, a considerable amount of pull requests (PRs) with valid contributions are abandoned by their contributors, wasting the effort and time put in by both the contributors and maintainers. To better understand the underlying dynamics of contributor-abandoned PRs, we conduct a mixed-methods study using both quantitative and qualitative methods. We curate a dataset consisting of 265,325 PRs including 4,450 abandoned ones from ten popular and mature GitHub projects and measure 16 features characterizing PRs, contributors, review processes, and projects. Using statistical and machine learning techniques, we find that complex PRs, novice contributors, and lengthy reviews have a higher probability of abandonment and the rate of PR abandonment fluctuates alongside the projects' maturity or workload. To identify why contributors abandon their PRs, we also manually examine a random sample of 354 abandoned PRs. We observe that the most frequent abandonment reasons are related to the obstacles faced by contributors, followed by the hurdles imposed by maintainers during the review process. Finally, we survey the top core maintainers of the studied projects to understand their perspectives on dealing with PR abandonment and on our findings.