Better Scientific Software

The computational science and engineering (CSE) community is in the midst of an extremely challenging period created by the confluence of disruptive changes in computing architectures, demand for greater scientific reproducibility, and new opportunities for greatly improved simulation capabilities, especially through coupling physics and scales. Computer architecture changes require new software design and implementation strategies, including significant refactoring of existing code. Reproducibility demands require more rigor across the entire software endeavor and for running computational experiments. These challenges demand large investments in scientific software development and improved practices. Focusing on improved developer productivity and software sustainability is both urgent and essential. This tutorial will provide information and illustrative use cases of software practices, processes, and tools explicitly tailored for CSE. Goals are improving the productivity of those who develop CSE software and increasing the quality and sustainability of software artifacts. We discuss practices that are relevant for projects of all sizes, with emphasis on complex workflows and reproducible science. Topics include software design, refactoring, software testing (including automated testing, legacy code testing, and continuous integration), and methodologies for running reproducible computational experiments.