CamFort: Automated evolution and verification of computational science models

Scientific models play a vital role in science and policy making. Many models are now expressed as complex computer programs which are often the result of decades of research and development, possibly involving multiple researchers or teams. This has lead to significant investment in maintaining these models and evolving them to use modern programming approaches or to work efficiently on new hardware platforms (such as cloud computing resources). However, the complexity of these models makes maintenance and evolution difficult. In particular, changing a complex model's code whilst ensuring it produces the same results is difficult; maintenance/evolution of complex models is often error prone. The complexity of a piece of software can be classified as either intrinsic or accidental. Intrinsic complexity is an essential reflection of the complexity inherent in the problem and solution at hand. Alternatively, accidental complexity arises from the particular programming language, design or tools used to implement the solution. Many of the research contributions of programming language design and software engineering have been aimed at reducing the accidental complexity of software. However, many of these approaches have not been targetted at scientific computing. There is now a need to develop these contributions so that they meet the needs of scientists. Addressing these needs will provide huge benefits to science and policy through increased productivity and trust in models. Our collaborations with leading research groups in science have highlighted the huge existing investments in established models. We are therefore aiming to support the evolution, rather than replacement, of existing code and working practices. Our goal is to apply cutting edge programming language and software engineering research to help develop "sustainable" software, which maintains its value over generations of researcher. Our focus is on models developed in the Fortran language, as this remains a dominant programming language in scientific computing, owing in part to its longevity. We will provide practical tools which scientists can use to reduce the accidental complexity of models through evolving a code base, as well as tools for automatically verifying that any maintenance/evolution activities preserve the models behaviour. We will develop new mechanisms for program comprehension and transformation in order to bring effective techniques from programming language design and software engineering across the chasm to scientific computing. Ultimately, reducing the effort to maintain and evolve code will free-up scientists to focus on the core aspects of the science, and will lead to models that are more easily communicated, disseminated, and reused between researchers, supporting core ideals of science.