文件名称：Software.Engineering.for.Science.1498743854
文件大小：16.96MB
文件格式：PDF
更新时间：2019-12-14 10:37:21
Science Software Engineering
Software Engineering for Science provides an in-depth collection of peer-reviewed chapters that describe experiences with applying software engineering practices to the development of scientific software. It provides a better understanding of how software engineering is and should be practiced, and which software engineering practices are effective for scientific software. The book starts with a detailed overview of the Scientific Software Lifecycle, and a general overview of the scientific software development process. It highlights key issues commonly arising during scientific software development, as well as solutions to these problems. The second part of the book provides examples of the use of testing in scientific software development, including key issues and challenges. The chapters then describe solutions and case studies aimed at applying testing to scientific software development efforts. The final part of the book provides examples of applying software engineering techniques to scientific software, including not only computational modeling, but also software for data management and analysis. The authors describe their experiences and lessons learned from developing complex scientific software in different domains. About the Editors Jeffrey Carver is an Associate Professor in the Department of Computer Science at the University of Alabama. He is one of the primary organizers of the workshop series on Software Engineering for Science (http://www.SE4Science.org/workshops). Neil P. Chue Hong is Director of the Software Sustainability Institute at the University of Edinburgh. His research interests include barriers and incentives in research software ecosystems and the role of software as a research object. George K. Thiruvathukal is Professor of Computer Science at Loyola University Chicago and Visiting Faculty at Argonne National Laboratory. His current research is focused on software metrics in open source mathematical and scientific software. Table of Contents Chapter 1 Software Process for Multiphysics Multicomponent Codes Chapter 2 A Rational Document Driven Design Process for Scientific Software Chapter 3 Making Scientific Software Easier to Understand, Test, and Communicate through Software Engineering Chapter 4 Testing of Scientific Software: Impacts on Research Credibility, Development Productivity, Maturation, and Sustainability Chapter 5 Preserving Reproducibility through Regression Testing Chapter 6 Building a Function Testing Platform for Complex Scientific Code Chapter 7 Automated Metamorphic Testing of Scientific Software Chapter 8 Evaluating Hierarchical Domain-Specific Languages for Computational Science: Applying the Sprat Approach to a Marine Ecosystem Model Chapter 9 Providing Mixed-Language and Legacy Support in a Library: Experiences of Developing PETSc Chapter 10 HydroShare – A Case Study of the Application of Modern Software Engineering to a Large Distributed Federally-Funded Scientific Software Development Project