Unknown

Dataset Information

0

Improving bioinformatics software quality through incorporation of software engineering practices


ABSTRACT:

Background

Bioinformatics software is developed for collecting, analyzing, integrating, and interpreting life science datasets that are often enormous. Bioinformatics engineers often lack the software engineering skills necessary for developing robust, maintainable, reusable software. This study presents review and discussion of the findings and efforts made to improve the quality of bioinformatics software.

Methodology

A systematic review was conducted of related literature that identifies core software engineering concepts for improving bioinformatics software development: requirements gathering, documentation, testing, and integration. The findings are presented with the aim of illuminating trends within the research that could lead to viable solutions to the struggles faced by bioinformatics engineers when developing scientific software.

Results

The findings suggest that bioinformatics engineers could significantly benefit from the incorporation of software engineering principles into their development efforts. This leads to suggestion of both cultural changes within bioinformatics research communities as well as adoption of software engineering disciplines into the formal education of bioinformatics engineers. Open management of scientific bioinformatics development projects can result in improved software quality through collaboration amongst both bioinformatics engineers and software engineers.

Conclusions

While strides have been made both in identification and solution of issues of particular import to bioinformatics software development, there is still room for improvement in terms of shifts in both the formal education of bioinformatics engineers as well as the culture and approaches of managing scientific bioinformatics research and development efforts.

SUBMITTER: Noor A 

PROVIDER: S-EPMC8771759 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC4869009 | biostudies-literature
| S-EPMC7305685 | biostudies-literature
| S-EPMC7924430 | biostudies-literature
| S-EPMC6755254 | biostudies-literature
| S-EPMC3693927 | biostudies-literature
| S-EPMC6457263 | biostudies-literature
2018-06-08 | GSE107767 | GEO
2018-06-08 | GSE107766 | GEO
| S-EPMC4917176 | biostudies-literature
| S-EPMC7924421 | biostudies-literature