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Increasing quality and managing complexity in neuroinformatics software development with continuous integration

机译:通过持续集成提高神经信息学软件开发的质量并管理其复杂性

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摘要

High quality neuroscience research requires accurate, reliable and well maintained neuroinformatics applications. As software projects become larger, offering more functionality and developing a denser web of interdependence between their component parts, we need more sophisticated methods to manage their complexity. If complexity is allowed to get out of hand, either the quality of the software or the speed of development suffer, and in many cases both. To address this issue, here we develop a scalable, low-cost and open source solution for continuous integration (CI), a technique which ensures the quality of changes to the code base during the development procedure, rather than relying on a pre-release integration phase. We demonstrate that a CI-based workflow, due to rapid feedback about code integration problems and tracking of code health measures, enabled substantial increases in productivity for a major neuroinformatics project and additional benefits for three further projects. Beyond the scope of the current study, we identify multiple areas in which CI can be employed to further increase the quality of neuroinformatics projects by improving development practices and incorporating appropriate development tools. Finally, we discuss what measures can be taken to lower the barrier for developers of neuroinformatics applications to adopt this useful technique.
机译:高质量的神经科学研究需要准确,可靠和维护良好的神经信息学应用程序。随着软件项目规模的扩大,提供更多功能以及在组件之间建立相互依赖的密集网络,我们需要更复杂的方法来管理其复杂性。如果让复杂性一发不可收拾,那么软件的质量或开发速度都会受到影响,并且在许多情况下都会受到影响。为了解决这个问题,我们在这里开发了一种可扩展的,低成本的,持续集成(CI)的开源解决方案,该技术可确保在开发过程中对代码库进行更改的质量,而不是依赖于预发行版整合阶段。我们证明,基于CI的工作流由于对代码集成问题的快速反馈和对代码健康措施的跟踪而使重大神经信息学项目的生产率显着提高,并为另外三个项目带来了额外收益。除了当前研究的范围之外,我们还确定了可以在其中使用CI的多个领域,这些领域可以通过改进开发实践并结合适当的开发工具来进一步提高神经信息学项目的质量。最后,我们讨论了可以采取哪些措施来降低神经信息学应用程序开发人员采用此有用技术的障碍。

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