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Almost all research work in computational neuroscience involves software. Asresearchers try to understand ever more complex systems, there is a continualneed for software with new capabilities. Because of the wide range of questionsbeing investigated, new software is often developed rapidly by individuals orsmall groups. In these cases, it can be hard to demonstrate that the softwaregives the right results. Software developers are often open about the code theyproduce and willing to share it, but there is little appreciation amongpotential users of the great diversity of software development practices andend results, and how this affects the suitability of software tools for use inresearch projects. To help clarify these issues, we have reviewed a range ofsoftware tools and asked how the culture and practice of software developmentaffects their validity and trustworthiness. We identified four key questionsthat can be used to categorize software projects and correlate them with thetype of product that results. The first question addresses what is beingproduced. The other three concern why, how, and by whom the work is done. Theanswers to these questions show strong correlations with the nature of thesoftware being produced, and its suitability for particular purposes. Based onour findings, we suggest ways in which current software development practice incomputational neuroscience can be improved and propose checklists to helpdevelopers, reviewers and scientists to assess the quality whether particularpieces of software are ready for use in research.

Current practice in software development for computational neuroscience and how to improve it