Computational software is the backbone of progress in many fields of science and engineering. As the importance and complexity of this software grows, developers should apply software development best practices systematically. However, exploratory, agile, and numerical codes present unique challenges.
Developers must combine cutting-edge expertise from applied mathematics, computer science, high-performance computing and domain-specific knowledge, relying on small, highly specialised research groups and individuals from diverse career levels, to maintain codebases across generations of team members, often working asynchronously across the globe.  These conditions demand bespoke development techniques tailored to the research environment.
This book presents a collection of best practices and lessons learned from the computational science community, offering critical observations on where mainstream development techniques struggle or fail in this specialised domain, or where mainstream software development technology is widely adopted yet might not be the best fit overall. The author challenges common recommendations for code development and suggests that there is a need to design and implement bespoke software development workflows that deliver research software capable of facilitating new insights in challenging research environments.
The discussion remains technically focused, avoiding high-level management theory, career development frameworks, and team dynamics discussions in favour of practical ideas that make day-to-day research code development more efficient, effective, and enjoyable.
The book is written specifically for practitioners who develop software as part of computational science research. It addresses techniques, recipes, and principles relevant to PhD students, research software engineers, postdoctoral researchers, and senior academics who mentor and instruct colleagues.