Scientific software development is not an oxymoron

By understanding similarities between these approaches, we can layer some practical methods from the software development life cycle onto computational biology projects to build a solid foundation for success. (In addition to the references cited, see Box 1 for a suggested library and for resources...

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Bibliographic Details
Published in:PLoS computational biology 2006-09, Vol.2 (9), p.e87-e87
Main Authors: Baxter, Susan M, Day, Steven W, Fetrow, Jacquelyn S, Reisinger, Stephanie J
Format: Article
Language:English
Subjects:
Bioinformatics - Computational Biology
Biomedical research
Computational biology
Computer programs
Design
Experiments
Forecasts and trends
Hypotheses
None
Quality control
Science
Science Policy
Software Design
Software industry
Studies
Success
Systems Biology
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Summary:By understanding similarities between these approaches, we can layer some practical methods from the software development life cycle onto computational biology projects to build a solid foundation for success. (In addition to the references cited, see Box 1 for a suggested library and for resources to improve scientific software development processes.) We define success as delivering a code base that produces consistent, reproducible results, is usable and useful, can be easily maintained and updated, and has a reasonable shelf life. Project management for a modest algorithm-development project involving one or two programmers might involve informal design and code reviews, regular meetings to track progress against an established timeline, and review (and sign-off) of testing results.
ISSN:1553-734X
1553-7358
DOI:10.1371/journal.pcbi.0020087