On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 12 of its 88 references that are in the map. First posted on arXiv 28 Oct 2021.
Builds on
12 of 88 references.
References · 12
- 2018All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneouslycited by 364
- 2016Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Modelscited by 212
- 2015ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and Rcited by 171
- 2018Hyperparameters and Tuning Strategies for Random Forestcited by 83
- 2016Understanding the Factors that Impact the Popularity of GitHub Repositoriescited by 27
- 2018What's in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platformcited by 25
- 2019Does Code Quality Affect Pull Request Acceptance? An empirical studycited by 9
- 2021Pull Request Latency Explained: An Empirical Overviewcited by 6
- 2020How Successful Are Open Source Contributions From Countries with Different Levels of Human Development?cited by 3
- 2021On the Relationship Between the Developer's Perceptible Race and Ethnicity and the Evaluation of Contributions in OSScited by 3
- 2021Pull Request Decision Explained: An Empirical Overviewcited by 2
- 2021GAP2WSS: A Genetic Algorithm based on the Pareto Principle for Web Service Selectioncited by 2
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Citing papers per year: 2023: 2; 2024: 3; 2025: 0; 2026: 1.
6 papers.
- 2023Predicting the First Response Latency of Maintainers and Contributors in Pull Requestscited by 3
- 2023Understanding the Helpfulness of Stale Bot for Pull-based Development: An Empirical Study of 20 Large Open-Source Projectscited by 2
- 2024Automatic Detection of LLM-Generated Code: A Comparative Case Study of Contemporary Models Across Function and Class Granularitiescited by 1
- 2024The Impact of Environment Configurations on the Stability of AI-Enabled Systemscited by 0
- 2024An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbotscited by 0
- 2026Understanding the Rejection of Fixes Generated by Agentic Pull Requests -- Insights from the AIDev Datasetcited by 0
- 2026Understanding the Rejection of Fixes Generated by Agentic Pull Requests -- Insights from the AIDev Dataset11 Jun 2026
- 2024An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbots9 Oct 2024
- 2024Automatic Detection of LLM-Generated Code: A Comparative Case Study of Contemporary Models Across Function and Class Granularities2 Sep 2024
- 2024The Impact of Environment Configurations on the Stability of AI-Enabled Systems5 Aug 2024
- 2023Predicting the First Response Latency of Maintainers and Contributors in Pull Requests13 Nov 2023
- 2023Understanding the Helpfulness of Stale Bot for Pull-based Development: An Empirical Study of 20 Large Open-Source Projects29 May 2023