What Drives Issue Resolution Speed? An Empirical Study of Scientific Workflow Systems on GitHub
Khairul Alam, Banani Roy
TL;DR
This paper investigates what drives issue-resolution speed in GitHub-hosted Scientific Workflow Systems (SWSs). It employs an empirical analysis of 21,116 issues across 197 repositories (Galaxy, Nextflow, Snakemake), examining issue- and contributor-related features and their relation to time-to-close. Key findings show a 68.91% overall issue-closure rate with a median time-to-close of 18.09 days, while labeling and assigning issues are linked to faster resolution; textual content and the number of contributors have negligible practical impact. The results highlight governance, triage, and task assignment as the main levers for responsiveness, with practical recommendations for maintainers and platform designers to reduce backlogs and improve SWS sustainability.
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.