Abstract
Experimental and observational sciences have developed robust practices for conducting experiments, maintaining their instruments, and record keeping for provenance. Computational science has only recently begun to confront the issue of quality of their instrument, the software, and the credibility of their scientific output. Most of the available literature in software engineering relates to enterprise software. While it can inform practices in scientific software, adjustments are usually needed. From time to time quality conscious practitioners have published collections of best practices for scientific software. This article provides one more such list but with updated suggestions, motivated by the need to keep up with the rapid changes in the computing industry.
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