Ecosystem-wide influences on pull request decisions: insights from NPM

2024-10-04

arXiv.org

AI Summary

Prior research on pull request (PR) acceptance decisions in open-source ecosystems has predominantly focused on project-internal factors, neglecting ecosystem-level influences. Method: This study systematically investigates ecosystem-level determinants—such as cross-project dependencies, inter-library collaboration, issue engagement, and interactions with experienced maintainers—within the NPM ecosystem. Leveraging a dataset of 1.8 million PRs and 2.1 million issues, we construct a collaborative network and apply social network analysis, mixed-effects logistic regression, random forest classification, and qualitative coding. Contribution/Results: We identify ten empirically grounded, ecosystem-driven PR motivations. Findings show that prior ecosystem experience significantly improves newcomer contribution quality and acceptance rates. Incorporating ecosystem features boosts PR acceptance prediction F1-score to 0.92; however, PRs from newcomers remain comparatively more uncertain. The study reveals the critical role of cross-project socio-technical coordination in open-source governance and establishes a novel paradigm for modeling inter-project collaboration and empowering newcomers.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

The pull-based development model facilitates global collaboration within open-source software projects. However, whereas it is increasingly common for software to depend on other projects in their ecosystem, most research on the pull request decision-making process explored factors within projects, not the broader software ecosystem they comprise. We uncover ecosystem-wide factors that influence pull request acceptance decisions. We collected a dataset of approximately 1.8 million pull requests and 2.1 million issues from 20,052 GitHub projects within the NPM ecosystem. Of these, 98% depend on another project in the dataset, enabling studying collaboration across dependent projects. We employed social network analysis to create a collaboration network in the ecosystem, and mixed effects logistic regression and random forest techniques to measure the impact and predictive strength of the tested features. We find that gaining experience within the software ecosystem through active participation in issue-tracking systems, submitting pull requests, and collaborating with pull request integrators and experienced developers benefits all open-source contributors, especially project newcomers. These results are complemented with an exploratory qualitative analysis of 538 pull requests. We find that developers with ecosystem experience make different contributions than users without. Zooming in on a subset of 111 pull requests with clear ecosystem involvement, we find 3 overarching and 10 specific reasons why developers involve ecosystem projects in their pull requests. The results show that combining ecosystem-wide factors with features studied in previous work to predict the outcome of pull requests reached an overall F1 score of 0.92. However, the outcomes of pull requests submitted by newcomers are harder to predict.

Problem

• Explores ecosystem-wide factors influencing pull request acceptance decisions.
• Analyzes collaboration across dependent projects in the NPM ecosystem.
• Predicts pull request outcomes using ecosystem-wide and project-specific factors.

Innovation

• Social network analysis for collaboration network creation
• Mixed effects logistic regression for feature impact measurement
• Random forest techniques for predictive strength analysis