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

TL;DR

This paper reviews software development practices in computational neuroscience, analyzing how these practices impact software validity and proposing checklists to improve software quality and trustworthiness in research.

Contribution

It introduces a framework of four key questions to categorize software projects and correlates development practices with software suitability for research purposes.

Findings

Development practices strongly influence software validity.

Categorization framework helps assess software trustworthiness.

Proposed checklists aim to improve software quality in neuroscience.

Abstract

Almost all research work in computational neuroscience involves software. As researchers try to understand ever more complex systems, there is a continual need for software with new capabilities. Because of the wide range of questions being investigated, new software is often developed rapidly by individuals or small groups. In these cases, it can be hard to demonstrate that the software gives the right results. Software developers are often open about the code they produce and willing to share it, but there is little appreciation among potential users of the great diversity of software development practices and end results, and how this affects the suitability of software tools for use in research projects. To help clarify these issues, we have reviewed a range of software tools and asked how the culture and practice of software development affects their validity and trustworthiness. We identified four key questions that can be used to categorize software projects and correlate them with the type of product that results. The first question addresses what is being produced. The other three concern why, how, and by whom the work is done. The answers to these questions show strong correlations with the nature of the software being produced, and its suitability for particular purposes. Based on our findings, we suggest ways in which current software development practice in computational neuroscience can be improved and propose checklists to help developers, reviewers and scientists to assess the quality whether particular pieces of software are ready for use in research.