On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests–A Mixed-Methods Study of 10 Large Open-Source Projects

Abstract

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.

Team Size Dynamics: A Study on Factors Influencing Pull Request Acceptance in Open-Source Software Repositories

“…first_pr 2. core_member 3. test_inclusion For the Chi-Square Test, to calculate practical significance, we will use the measure Phi [11] and the measure Odds Ratio [12] when the chi-square table is of size 2x2; otherwise, we will use Cramer's V [11]. For the Mann-Whitney test, we will use Cliff's Delta [13] to derive practical significance, similar to the method used to evaluate features between completed and abandoned pull requests [1]. It's crucial to note that the Mann-Whitney U test is deemed suitable for our data distribution, as examined through the Shapiro-Wilk test [14] and by visually inspecting Histogram diagrams, revealing that the distribution of our datasets is not normal.…”

“…Pull-Based Development, as a widely adopted methodology, offers a wealth of insights into collaborative software development. Efforts to understand the complex process of reviewing and deciding on specific pull requests have been extensive, driven by the need to optimize time for both reviewers and contributors [1]. Platforms like GitHub, serving as vast repositories of open-source projects, provide invaluable data for scholarly exploration.…”