Source-linked AI summary
An Insight into the Pull Requests of GitHub
Mohammad Masudur Rahman, Chanchal K. Roy
TL;DR
The study responds to growing concerns about unsuccessful GitHub pull requests and the need to understand factors associated with their success and failure. The authors compare successful and failed requests by analyzing discussion texts, pull request histories, and project- and developer-specific information. On average, an issue was faced by 17.54% of 9,421 pull requests; 7.76% of requests succeeded to merge, while failure rates rose rapidly above 3,000 forks in seven projects.
Problem
The study responds to growing concerns about unsuccessful GitHub pull requests and the need to understand factors associated with their success and failure.
Source: §1 · p. 1Method
Results
On average, an issue was faced by 17.54% of 9,421 pull requests; 7.76% of requests succeeded to merge, while failure rates rose rapidly above 3,000 forks in seven projects.
Source: §5 · p. 4Takeaways & Limitations
The findings provide developers with insights into frequent technical issues and project administrators with information for managing pull requests, projects, and developers.
Source: §1 · pp. 1–2
Abstract
from arXiv · showhide
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.
1. INTRODUCTION
GitHub pull requests arise when developers submit work from forked repositories for merging, but not all succeed. The study compares merged and failed requests using discussion, project, and developer information.
1. INTRODUCTION
- Developers work in forks and submit completed milestones for merging, after which project members review the code and discuss concerns.Successful requests merge; unsuccessful requests fail to merge.Source: §1 · p. 1
- The study compares successful and unsuccessful pull requests using discussion texts, pull request history, and project- and developer-specific statistics.The analysis aims to provide insights into pull request success and failure.Source: §1 · p. 1
- Across 78 base projects, the study analyzes 78,517 pull requests and identifies frequent technical topics and language- and domain-related patterns.It also reports lower success rates for projects with more than 4,000 developers or 3,000 forked projects.Source: §1 · p. 1
- The findings are intended to help developers improve pull requests and help project administrators make informed management decisions.The paper highlights both developer- and project-level insights.Source: §1 · p. 2
2. DATASET
The dataset covers pull requests from 78 base projects and their forks, with discussion texts and project- and developer-level information used for comparison.
2. DATASET
- The MSR challenge dataset contains 78,955 pull requests from 88 base projects and 20,142 developers; the study limits its analysis to 78 base projects and their forks.The 78-project subset includes 9,601 requests with pull request commit comments.Source: §2 · p. 2
- The study collects 9,601 conversation documents with commit comments, including 4,091 merged and 5,510 failed requests, and also uses 68,916 requests without discussion.Each conversation sequence is treated as a document.Source: §2 · p. 2
- The project and developer data include domain, programming language, age, maturity, developer number, and experience across projects in 13 programming languages.The projects span application domains including frameworks and libraries, networking, databases, IDEs, and statistics.Source: §2 · p. 2
3. TOOLS AND METHODOLOGY
The study preprocesses pull request discussions for topic modeling, then labels selected topics manually for comparative analysis.
3. TOOLS AND METHODOLOGY
- After stop-word removal and stemming, the authors use 9,421 discussion documents, discarding 180 whose remaining content is insignificant.Documents are represented as collections of stemmed tokens.Source: §3 · p. 2
- JGibbLDA uses Gibbs sampling to model the corpus, yielding 100 topics described by ten relevant words and document-level topic probabilities for later comparison.The model uses 3,000 sampling iterations.Source: Figure 2 · p. 2, §3 · p. 2
- The authors manually label 64 of the 100 topics by selecting four representative words from each topic's list of ten relevant words.The selected words need not have the four highest probabilities.Source: §3 · p. 2, Figure 2 · p. 2
4. COMPARATIVE ANALYSIS
The analysis compares pull-request outcomes across discussion topics, project characteristics, and developer participation and experience. Unsuccessful requests are more prevalent for most topics and increase in some high-fork or large-team settings, while developers with 20 to 50 months of experience are reported as most productive.
Technical Topics Discussed
- Eight topics are widely discussed, including Recursion and Refactoring and Database Query Execution; each is more prevalent in unsuccessful requests except Actor Model.The six labeled topics also include Arrays and functions, Actor Model, OOP Paradigm, and Space and Indents.Source: §4 · p. 3
Programming Language
- Scala, C, C#, R, and PHP projects average more successful than failed requests, while JavaScript, Java, Python, Ruby, and C++ projects show the reverse pattern.PHP and Ruby projects have the highest monthly request counts on average, but Ruby projects show increasing failed requests per month.Source: §4 · p. 3
Application Domain
- Framework and IDE projects have relatively higher pull-request success rates than projects in other domains.Networking, Library, and Client Apps projects show comparatively similar success and failure rates.Source: §4 · p. 3
Project Age & Maturity
- After October 2009, monthly successful and failed requests rose almost exponentially; failure rates were higher especially for projects with more than 2,000 forks, despite irregular request-volume patterns.Fork counts are used as an estimate of project maturity.Source: Figure 4 · p. 3, §4 · p. 3, Figure 5 · p. 3
Project Developers & Experience
- Average monthly requests do not rise comparatively with developer participation, but projects with large developer crowds may make excessive numbers of unsuccessful requests.The study reports an exponentially increasing unsuccessful-request rate for one project with more than 4,000 developers involved.Source: Figure 6 · p. 4, §4 · p. 4
- Developers with 20 to 50 months of experience are reported as most productive; more experienced developers are either less productive or make more unsuccessful requests.The authors speculate that experienced developers may be doing management work, but the dataset does not provide role information.Source: Figure 7 · p. 4, §4 · p. 4
5. DISCUSSION AND CONCLUSION
The study contrasts successful and unsuccessful pull requests across discussion topics, project characteristics, and developer participation. It reports patterns in technical concerns, domains, project forks, and developer experience.
5. DISCUSSION AND CONCLUSION
- Across 9,421 pull requests, eight technical issues were faced on average by 17.54%, and 7.76% of requests succeeded to merge.Six issues were labeled; the other 92 topics each appeared in 3.90% of discussions.Source: §5 · p. 4
- Across 24 Ruby, Java, and JavaScript projects, unsuccessful pull requests averaged more per month than successful ones; excessive forking is proposed as a possible reason.The authors call for research into language-specific factors affecting pull-request outcomes.Source: §5 · p. 4
- IDE, Framework, and Client Apps projects had comparatively higher pull-request success, while Database and Statistics projects showed very limited activity.The study suggests limited project counts as a possible explanation for low activity and calls for research into domain-specific concerns.Source: §5 · p. 4
- As projects age and accumulate forks, pull requests and both outcomes increase; in seven projects, failure rates rise rapidly beyond 3,000 forks.The authors identify this pattern as potentially useful information for base-project administrators.Source: §5 · p. 4
- With greater developer participation, monthly pull-request volume rises almost regularly, while one project's unsuccessful rate grows exponentially above 4,000 developers; developers with 20–50 months' experience were most productive.The study cannot provide a suitable explanation for the experience finding because the dataset lacks information.Source: §5 · p. 4