ABSTRACT
Key Insights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.6 Generalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Key Insights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.7 Additional Future Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
1.1 Introduction Computational science and engineering communities develop complex ap-
plications to solve scientific and engineering challenges, but these communities have a mixed record of using software engineering best practices [43,296]. Many codes developed by scientific communities adopt standard software practices when the size and complexity of an application become too unwieldy to continue without them [30]. The driving force behind adoption is usually the realization that without using software engineering practices, the development, verification, and maintenance of applications can become intractable. As more codes cross the threshold into increasing complexity, software engineering processes are being adopted from practices derived outside the scientific and engineering domain. Yet the state of the art for software engineering practices in scientific codes often lags behind that in the commercial software space [16, 36, 52]. There are many reasons: lack of incentives, support, and funding; a reward system favoring scientific results over software development; limited understanding of how software engineering should be promoted to communities that have their own specific needs and sociology [22,35].
