Test Driven Development: Lessons from a Simple Scientific Model

In the commercial software industry, unit testing frameworks have emerged as a disruptive technology that has permanently altered the process by which software is developed. Unit testing frameworks significantly reduce traditional barriers, both practical and psychological, to creating and executing tests that verify software implementations. A new development paradigm, known as test driven development (TDD), has emerged from unit testing practices, in which low-level tests (i.e. unit tests) are created by developers prior to implementing new pieces of code. Although somewhat counter-intuitive, this approach actually improves developer productivity. In addition to reducing the average time for detecting software defects (bugs), the requirement to provide procedure interfaces that enable testing frequently leads to superior design decisions. Although TDD is widely accepted in many software domains, its applicability to scientific modeling still warrants reasonable skepticism. While the technique is clearly relevant for infrastructure layers of scientific models such as the Earth System Modeling Framework (ESMF), numerical and scientific components pose a number of challenges to TDD that are not often encountered in commercial software. Nonetheless, our experience leads us to believe that the technique has great potential not only for developer productivity, but also as a tool for understanding and documenting the basic scientific assumptions upon which our models are implemented. We will provide a brief introduction to test driven development and then discuss our experience in using TDD to implement a relatively simple numerical model that simulates the growth of snowflakes. Many of the lessons learned are directly applicable to larger scientific models.