5 posts tagged with "testing"

In Part 2 of our CI/CD series, we showed how to set up automated testing with make test-e2e—running your workflow with test data and checking if it produces results. If the script runs without crashing, you might think "everything works fine." But here's the uncomfortable truth: a pipeline that runs successfully doesn't mean it produces correct results.

This post explains why testing goes far beyond "running code and checking it doesn't crash." We'll explore the different types of tests bioinformaticians should care about and show practical examples of how to catch real bugs that simple end-to-end tests would miss.

Creating and maintaining a library of reusable Nextflow modules is a significant challenge for bioinformatics teams. Without a consistent structure, code quality standards, and automated testing, modules quickly become difficult to share, validate, and integrate into pipelines. The nf-modules-template solves this by providing a production-ready template that uses Copier to scaffold new module repositories, GitHub Actions for automated CI/CD workflows, pre-commit hooks for code quality, and nf-test with intelligent sharding for scalable module testing. This post explores how these technologies work together to enable reproducible, maintainable Nextflow module libraries.

Testing Ansible playbooks for HPC clusters is challenging. You can manually spin up VMs with Vagrant, but debugging issues across controller and worker nodes takes time. Instead, Molecule provides a repeatable, automated testing framework that validates your playbook configuration before deployment. This post shows how to transition from manual Vagrant testing to Molecule, and then integrate it into GitHub Actions CI/CD.

Whether your lab uses bash scripts, Python workflows, Snakemake pipelines, or custom solutions—your in-house pipeline works fine locally. It's been running for years. But as your research scales, you face a hard truth: in-house pipelines don't scale, aren't reproducible across teams, and require constant manual fixes.

Building reproducible bioinformatics pipelines is hard. Every project starts from scratch with its own testing, CI/CD, and deployment strategy. What if you could clone a template, add your analysis tools, and be ready to go?

This post introduces a standardized bioinformatics workflow template featuring consistent testing, CI/CD, and project structure. Developed from real production experience with bioinfor-wf-template, this template reduces setup time from days to minutes, ensures research reproducibility, and promotes modular, reusable code. It is Python-based and ideal for proof-of-concept projects. Support for more advanced and widely adopted bioinformatics frameworks (such as Snakemake and Nextflow) is planned, applying the same core principles while leveraging their native testing systems.