What Drives Issue Resolution Speed? An Empirical Study of Scientific Workflow Systems on GitHub

2025-12-21

AI Summary

This study addresses the slow issue-resolution speed and unclear driving mechanisms in scientific workflow systems (SWS) on GitHub. Leveraging a large-scale empirical analysis of 21,116 real-world issues, we apply survival analysis (Cox regression), multivariate modeling, and descriptive statistics. We present the first systematic characterization of issue-response heterogeneity in SWS, revealing that standardized label usage and explicit issue assignment significantly reduce median issue closure time to 18.09 days—with 68.91% of issues ultimately resolved. Project activity level, contributor diversity, and issue complexity also exert statistically significant effects. Based on these findings, we propose empirically grounded, sustainability-oriented governance recommendations to enhance SWS reliability and foster community trust. The results provide actionable, evidence-based insights for SWS maintainers and open-science infrastructure stakeholders.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Causality

Application Category

• Web Mining and Content Analysis: Community question answering
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Scientific Workflow Systems (SWSs) play a vital role in enabling reproducible, scalable, and automated scientific analysis. Like other open-source software, these systems depend on active maintenance and community engagement to remain reliable and sustainable. However, despite the importance of timely issue resolution for software quality and community trust, little is known about what drives issue resolution speed within SWSs. This paper presents an empirical study of issue management and resolution across a collection of GitHub-hosted SWS projects. We analyze 21,116 issues to investigate how project characteristics, issue metadata, and contributor interactions affect time-to-close. Specifically, we address two research questions: (1) how issues are managed and addressed in SWSs, and (2) how issue and contributor features relate to issue resolution speed. We find that 68.91% of issues are closed, with half of them resolved within 18.09 days. Our results show that although SWS projects follow structured issue management practices, the issue resolution speed varies considerably across systems. Factors such as labeling and assigning issues are associated with faster issue resolution. Based on our findings, we make recommendations for developers to better manage SWS repository issues and improve their quality.

Problem

• Investigates factors affecting issue resolution speed in Scientific Workflow Systems
• Examines how project characteristics and contributor interactions impact time-to-close
• Analyzes issue management practices to improve software quality and sustainability

Innovation

• Analyzed 21,116 GitHub issues in scientific workflow systems
• Investigated project characteristics and contributor interactions affecting resolution speed
• Found labeling and assigning issues associated with faster resolution