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Software engineering principles to improve quality and performance of R software.
Seth Russell1, Tellen D Bennett1,2, Debashis Ghosh1,3
1University of Colorado Data Science to Patient Value, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Peerj. Computer Science
|April 5, 2021
Summary
Computational researchers often lack software engineering training. A survey of R packages reveals a lack of software testing and optimization, highlighting the need for improved software quality in scientific research.
Area of Science:
- Computational science
- Software engineering
- Bioinformatics
Background:
- Computational researchers require strong software skills for data analysis and personalized medicine.
- Formal training in software engineering principles, particularly testing and optimization, is often lacking.
- Existing literature and university coursework inadequately address these crucial software development aspects.
Purpose of the Study:
- To assess the prevalence of software testing and optimization in R packages.
- To identify gaps in current software development practices among computational researchers.
- To demonstrate the benefits of applying software testing and optimization techniques.
Main Methods:
- Surveyed all available Comprehensive R Archive Network (CRAN) packages.
- Analyzed packages for the presence of reproducible and replicable software tests.
- Examined packages for the use of software performance and optimization tools.
- Utilized examples from an R package to illustrate testing and optimization methods.
Main Results:
- Reproducible and replicable software tests were frequently unavailable in CRAN packages.
- Many packages did not appear to implement software performance and optimization techniques.
- Demonstrated that testing and optimization can significantly enhance software quality.
Conclusions:
- There is a significant deficit in software testing and optimization practices within the R package ecosystem.
- Implementing robust software engineering principles is essential for improving the reliability and efficiency of computational research tools.
- Researchers can improve their software quality by adopting demonstrated testing and optimization strategies.
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