When Software Engineers Met Research Scientists: A Case Study
Empirical Software Engineering
Software Development Environments for Scientific and Engineering Software: A Series of Case Studies
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Developing Scientific Software
IEEE Software
Dealing with Risk in Scientific Software Development
IEEE Software
Semantic middleware for e-science knowledge spaces
Proceedings of the 7th International Workshop on Middleware for Grids, Clouds and e-Science
A survey of scientific software development
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
A literature review of agile practices and their effects in scientific software development
Proceedings of the 4th International Workshop on Software Engineering for Computational Science and Engineering
A domain specific requirements model for scientific computing (NIER track)
Proceedings of the 33rd International Conference on Software Engineering
The best of most worlds: shared objects for multilingual simulation
Proceedings of the 9th Workshop on Parallel/High-Performance Object-Oriented Scientific Computing
A survey of the practice of computational science
State of the Practice Reports
Hi-index | 0.00 |
New knowledge in science and engineering relies increasingly on results produced by scientific software. Therefore, knowing how scientists develop and use software in their research is critical to assessing the necessity for improving current development practices and to making decisions about the future allocation of resources. To that end, this paper presents the results of a survey conducted online in October-December 2008 which received almost 2000 responses. Our main conclusions are that (1) the knowledge required to develop and use scientific software is primarily acquired from peers and through self-study, rather than from formal education and training; (2) the number of scientists using supercomputers is small compared to the number using desktop or intermediate computers; (3) most scientists rely primarily on software with a large user base; (4) while many scientists believe that software testing is important, a smaller number believe they have sufficient understanding about testing concepts; and (5) that there is a tendency for scientists to rank standard software engineering concepts higher if they work in large software development projects and teams, but that there is no uniform trend of association between rank of importance of software engineering concepts and project/team size.