Assessing the Quality of Research Data Infrastructure Software

科学正在迅速发展，包括自动驾驶汽车，高通量科学仪器，高保真数值模型和传感器网络等技术，所有这些都以越来越高的频率，种类和数量生成数据。科学家有兴趣分享这些数据（或被迫分享），这需要研究数据基础设施（RDI：数字基础设施，旨在促进数据共享和消费，以支持研究工作）。RDI软件通常是自主开发的：由没有接受过软件工程正式培训的人创建和部署，或者在软件开发以外的组织中进行。这些开发人员通常也是用户;当他们或他们的同事确定需要时，他们会添加功能。我们对软件工程作为一个领域和实践的理解并不能普遍地转化为这个软件。该软件被越来越多的用户使用，但没有人系统地评估其可维护性，寿命，技术债务，社区弹性或其他长期健康指标，也不知道评估这些指标的现有方法是否对RDI软件有效。随着RDI的重要性和复杂性的增长，我们必须对其可靠性和准确性充满信心。我们知道非典型的开发过程可以产生高质量的结果，但我们也知道RDI软件正在被对大规模性能、自动数据清理和可视化等功能的新期望所拉伸。这个项目将应用软件质量度量的可维护性，技术债务，可持续性，并使用RDI软件的最佳实践。对于专业开发人员创建的软件的这些指标已经有了广泛的研究。我们将确定最相关的指标和工具，并将其应用于基于RDI的海洋科学案例研究。我们将进行人种学研究，将指标告诉我们的内容与用户社区和开发人员的实际体验进行比较。根据结果，我们将推荐运行良好并产生符合实际情况的结果的指标。软件质量度量只有在开发人员意识到使用它们的好处时，才会对RDI软件的质量产生实际影响。我们将研究这些指标是如何感知和集成到RDI软件的开发过程中，与现有的开源，商业和科学软件的研究相比。好的科学需要好的数据。只有当我们拥有可维护、可靠和可用的研究数据基础设施时，才能实现可查找、可扩展、可互操作和可重用的数据。虽然RDI目前有些支离破碎，但其长期愿景是建立一个跨学科和跨国界的研究数据连接的全球网络。一个必要的先决条件是确保我们的软件能够完成这项任务。该项目将大大有助于这一需求，同时也推进经验软件工程研究。

Science is rapidly evolving, incorporating technology like autonomous vehicles, high-throughput scientific instruments, high-fidelity numerical models, and sensor networks, all generating data with increasing frequency, variety, and volume. Scientists are interested in sharing this data (or compelled to), which requires research data infrastructure (RDI: digital infrastructure organized to promote data sharing and consumption in support of research efforts). RDI software is often homegrown: created and deployed by people who have not received formal training in software engineering, or at organizations with primary mandates other than software development. These developers are often also users; they are adding features as they or their colleagues identify the need. Our understanding of software engineering as a field and practice does not universally translate to this software. This software is used by a growing set of users, but no one has systematically assessed its maintainability, longevity, technical debt, community resilience, or other indicators of long-term health, nor is it known if existing approaches to assessing these metrics are effective for RDI software. As RDI grows in importance and complexity, we must be confident in its reliability and accuracy. We know that atypical development processes can yield high-quality results, but we also know that RDI software is being stretched by new expectations for features like performance at scale, automated data cleaning, and visualization. This project will apply software quality metrics on maintainability, technical debt, sustainability, and the use of best practices to RDI software. There has been extensive research on these metrics for software created by professional developers. We will identify the most relevant metric and tools and apply them to case studies based on RDI for ocean science. We'll conduct ethnographic studies to compare what the metrics tell us with the lived experience of user communities and developers. Based on the results, we'll recommend metrics that work well and produce results that match reality. Software quality metrics will only have a practical impact on the quality of RDI software if developers realize benefit from using them. We will study how these metrics are perceived and integrated into development processes for RDI software, in comparison to existing studies on open source, commercial, and scientific software. Good science needs good data. Findable, Accessible, Interoperable, and Reusable data is only achievable if we have a research data infrastructure that is maintainable, reliable, and useable. While RDI is somewhat fractured at present, the long-term vision is a global network that links research data across disciplines and borders. A necessary precursor is ensuring that our software is up to this task. This project will contribute significantly to this need, while also advancing empirical software engineering research.