The reproduction and replication of novel results has become a major issue for a number of scientific disciplines. In computer science and related computational disciplines such as systems biology, the issues closely revolve around the ability to implement novel algorithms and models. Taking an approach from the literature and applying it to a new codebase frequently requires local knowledge missing from the published manuscripts and project websites. Alongside this issue, benchmarking, and the development of fair -- and publicly available -- benchmark sets present another barrier. In this paper, we outline several suggestions to address these issues, driven by specific examples from a range of scientific domains. Finally, based on these suggestions, we propose a new open automated platform for scientific software development which effectively abstracts specific dependencies from the individual researcher and their workstation, allowing easy sharing and reproduction of results. This new cyberinfrastructure for computational science offers the potential to incentivise a culture change and drive the adoption of new techniques to improve the efficiency of scientific exploration.