EAGER: Empirical Software Engineering for Computational Science

Science has become increasingly reliant on Computational Science methods implemented in software. These methods are complex, and therefore the software that implements them is prone to errors. This projects seeks to transformatively improve the state of the practice in the development of Computational Science software by applying systematic, data-driven methods (known as empirical methods) to evaluate how software is being developed and to suggest improvements. Improving the software engineering methods of Computational Science would result in higher quality software, and consequently increase our confidence in the research in scientific phenomena conducted by Computational Scientists,

Much of the work in Computational Science is related to the software that implements it. In this project, the researcher will apply state of the art empirical software engineering methods to Computational Science software. Qualitative methods will be applied to conduct large scale surveys of computational science. Quantitative data mining tools (classifiers, intelligent data preprocessor, automatic hyperparameter optimizers) will be used to can learn predictive models of time series of SE data such as 'Where in this system should we look for current bugs?' and 'How many bugs are left on the system?'. These models can be used to guide developer effort in building new code or maintaining old code.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.