Erika S. Mesh

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Scientific research is hard enough; software shouldn't make it harder. While traditional software engineering development and management practices have been shown to be effective in scientific software projects, adoption of these practices has been limited. Rather than presume to create a prescriptive scientific software process improvement manual or leave(More)
As the complexity of scientific software increases, scientists are often expected to be experts in their own domain as well as in the construction and maintenance of their software. To support this paradigm, numerous specialized approaches have emerged. Leveraging the underlying expertise and motivational factors that drive scientific software development(More)
  • Erika S. Mesh
  • 2015 IEEE/ACM 37th IEEE International Conference…
  • 2015
The increasing complexity of scientific software can result in significant impacts on the research itself. In traditional software development projects, teams adopt historical best practices into their development processes to mitigate the risk of such problems. In contrast, the gap that has formed between the traditional and scientific software communities(More)
Software engineers learn through experience and training to optimize their software development process through a dynamic cycle of assessment, planning, enactment, and evaluation. When needed, software process improvement (SPI) frameworks (SPIFs) fill in experiential and educational gaps. However, because of the needs that drove their original development,(More)
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