Analyzing GitHub Issues and Pull Requests in nf-core Pipelines: Insights into nf-core Pipeline Repositories

2026-01-14

AI Summary

This study addresses the lack of systematic understanding regarding the challenges users encounter in developing and maintaining nf-core standardized bioinformatics pipelines. Conducting the first large-scale empirical analysis, we examined 25,173 GitHub issues and pull requests using BERTopic for topic modeling, Cohen’s δ effect size statistics, and data mining techniques, identifying 13 key problem categories spanning core challenges such as tool development, CI configuration, and containerization debugging. Our findings reveal that 89.38% of reported issues were ultimately resolved, with half addressed within three days. Furthermore, the presence of issue labels and code snippets significantly enhanced resolution efficiency, offering empirical evidence to inform strategies for improving the sustainability and collaborative effectiveness of nf-core pipelines.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Scientific Workflow Systems (SWSs) such as Nextflow have become essential software frameworks for conducting reproducible, scalable, and portable computational analyses in data-intensive fields like genomics, transcriptomics, and proteomics. Building on Nextflow, the nf-core community curates standardized, peer-reviewed pipelines that follow strict testing, documentation, and governance guidelines. Despite its widespread adoption, little is known about the challenges users face in developing and maintaining these pipelines. This paper presents an empirical study of 25,173 issues and pull requests from these pipelines to uncover recurring challenges, management practices, and perceived difficulties. Using BERTopic modeling, we identify 13 key challenges, including pipeline development and integration, bug fixing, integrating genomic data, managing CI configurations, and handling version updates. We then examine issue-resolution dynamics, showing that 89.38\% of issues and pull requests are eventually closed, with half resolved within 3 days. Statistical analysis reveals that the presence of labels (large effect, $\mathit{d} = 0.94$) and code snippets (medium effect, $\mathit{d} = 0.50$) significantly improves the likelihood of resolution. Further analysis reveals that tool development and repository maintenance poses the most significant challenges, followed by testing pipelines and CI configurations, and debugging containerized pipelines. Overall, this study provides actionable insights into the collaborative development and maintenance of nf-core pipelines, highlighting opportunities to enhance their usability, sustainability, and reproducibility.

Problem

• nf-core
• scientific workflow
• pipeline maintenance
• GitHub issues
• reproducibility

Innovation

• BERTopic modeling
• empirical study
• nf-core pipelines
• issue resolution dynamics
• scientific workflow management