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Ten simple rules on writing clean and reliable open-source scientific software.
Haley Hunter-Zinck1,2,3,4, Alexandre Fioravante de Siqueira1, Váleri N Vásquez1,5
1Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, California, United States of America.
Plos Computational Biology
|November 11, 2021
Summary
This study introduces 10 rules for clean code and testing to improve open-source scientific software. These practices enhance code readability, maintainability, and reproducibility for researchers.
Area of Science:
- Computer Science
- Scientific Computing
- Software Engineering
Background:
- Open-source software is critical for scientific research.
- There's a lack of formal training in software development and maintainability for scientists.
- This variability impacts the quality and reliability of scientific software.
Discussion:
- Proposes 10 rules focusing on clean code and testing practices.
- Clean code enhances readability, reduces cognitive load, and standardizes software organization.
- Testing, including unit tests, verifies code functionality, identifies errors, and aids in debugging complex codebases.
Key Insights:
- Clean code practices make software more amenable to effective testing.
- Unit tests ensure modularity, isolate behavior, and provide usage examples.
- Implementing these practices improves software correctness, quality, usability, and maintainability.
- Foundational tools like clean code and testing are essential for reproducible scientific results.
Outlook:
- Suggests practical tips for adopting clean code and testing.
- Recommends specific tools for Python, R, and Julia.
- Aims to improve the overall quality and reliability of open-source scientific software.
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