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The State of Software for Evolutionary Biology
Diego Darriba1, Tomáš Flouri1, Alexandros Stamatakis1,2
1Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.
Molecular Biology and Evolution
|February 1, 2018
Summary
Scientific software quality in evolutionary biology is poor. This study assesses 16 core tools, finding unsatisfying software engineering quality and recommending best practices for reliable scientific software development.
Area of Science:
- Evolutionary biology
- Computational biology
- Bioinformatics
Background:
- Next Generation Sequencing (NGS) has transformed evolutionary biology into a computational science.
- Researchers increasingly rely on complex software tools for data analysis.
- The software engineering quality of these essential tools has been largely overlooked.
Purpose of the Study:
- To assess the software engineering quality of widely used, compute-intensive evolutionary biology tools.
- To identify areas for improvement in scientific software development.
- To advocate for better funding and practices in scientific software.
Main Methods:
- Analysis of 16 highly cited, core evolutionary biology software tools (C/C++, JAVA).
- Evaluation focused on software engineering quality and complexity.
- Identification of best practices for developing and maintaining scientific software.
Main Results:
- The software engineering quality of the analyzed tools is generally unsatisfying.
- Significant complexity and potential negative consequences for end-users were observed.
- Lack of attention to code quality in widely used bioinformatics software.
Conclusions:
- There is a substantial need to improve software engineering quality in evolutionary biology tools.
- Recommendations include adopting best practices and addressing funding for scientific software development.
- Raising community awareness on software quality is crucial for reliable scientific research.
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