Coding Smart in Academia: Evidence-Based Software Engineering Approaches for High-Quality Research Software Projects

学术界的智能编码：高质量研究软件项目的基于证据的软件工程方法

• 批准号: 2662705
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2662705
• 关键词: Coding; Smart; Academia; Evidence; Based

Research software describes any code written or software used in the process of generating results for a publication - for example, a script used to process raw data or carry out a statistical analysis - and is commonly written by researchers without a software engineering background or extensive experience. Inaccuracies or inefficiencies in research software impact the research that relies on it. While other tools used for research might be calibrated, tested, or evaluated in detail, software can often be overlooked, and problems might not be identified. This project will investigate research software from three key areas of focus (software engineering techniques, software quality, and research software project properties), to gain a better understanding of the software engineering techniques used to develop it, how to best assess the quality of research software and which research software project factors are influential in developing high quality outputs. It will consider whether insights or techniques used widely within traditional software development could be of use within research software development. The project will also gather empirical data from selected research software projects to identify evidence-based recommendations for the research software engineering community to help build better-quality research software. To achieve these aims, this project will assess methods for evaluating research software quality and consider the contexts and properties of projects developing research software, identifying how these might impact the software generated. Most previous research and literature have dealt with 'scientific software'. This project will address the wider area of 'research software' - a more inclusive and interdisciplinary approach which also captures practices from software used for research within the arts and humanities. Previous in-depth studies took place before widespread institutional support of research software projects and the emergence of a 'research software engineer' role. This study will update the literature on whether these landscape shifts indicate changes in practice. Many existing studies covering research software offer recommendations regarding best practices, however, little empirical evidence (particularly qualitative experimental data) is given to demonstrate that these recommendations are effective, while the range of research software projects they can be validly applied to is not clearly shown. To address this evidence gap, this project will identify potential improvements and more effective software engineering methods or techniques currently under-used within research software projects. These suggested interventions will be tested for their impacts within a representative set of research software projects. A 'control' group of closely matched projects (for example: similar software engineering technique usage, baseline research software quality, and project contexts such as development team sizes) will not receive an intervention. Data about software development practices and project contexts will be gathered from both groups of participating projects via surveys and interviews over the study period. Research software quality within these selected projects will also be measured throughout the experimental period to assess the impacts of these interventions on software quality. The recommended interventions will be refined based on this experimental testing, generating robust evidence-based and verified solutions to current research software challenges encountered by a range of research software projects. The outcomes of this study will allow researchers to identify which software engineering techniques and methods are most effective and would suit their project (listing known benefits or drawbacks) and demonstrate how to assess and improve the quality of their research software, with all suggestions strongly supported by data.

研究软件描述了在为出版物生成结果的过程中编写的任何代码或使用的软件 - 例如，用于处理原始数据或进行统计分析的脚本 - 通常由没有软件工程背景或丰富经验的研究人员编写。研究软件的不准确或低效率会影响依赖它的研究。虽然用于研究的其他工具可能经过详细校准、测试或评估，但软件往往会被忽视，并且可能无法识别问题。该项目将从三个关键领域（软件工程技术、软件质量和研究软件项目属性）研究研究软件，以更好地了解用于开发它的软件工程技术、如何最好地评估研究质量软件以及哪些研究软件项目因素对开发高质量产出有影响。它将考虑传统软件开发中广泛使用的见解或技术是否可以在研究软件开发中使用。该项目还将从选定的研究软件项目中收集经验数据，为研究软件工程社区确定基于证据的建议，帮助构建质量更高的研究软件。为了实现这些目标，该项目将评估评估研究软件质量的方法，并考虑开发研究软件的项目的背景和属性，确定这些可能如何影响生成的软件。大多数先前的研究和文献都涉及“科学软件”。该项目将涉及更广泛的“研究软件”领域——一种更具包容性和跨学科的方法，它还捕捉用于艺术和人文学科研究的软件的实践。先前的深入研究是在研究软件项目获得广泛的机构支持和“研究软件工程师”角色出现之前进行的。这项研究将更新有关这些景观变化是否表明实践变化的文献。许多涉及研究软件的现有研究提供了有关最佳实践的建议，然而，很少有经验证据（特别是定性实验数据）来证明这些建议是有效的，而它们可以有效应用的研究软件项目的范围也没有明确显示。为了解决这一证据差距，该项目将确定潜在的改进和更有效的软件工程方法或目前在研究软件项目中未充分使用的技术。这些建议的干预措施将在一组具有代表性的研究软件项目中测试其影响。紧密匹配的项目的“控制”组（例如：类似的软件工程技术使用、基线研究软件质量以及开发团队规模等项目背景）将不会受到干预。有关软件开发实践和项目背景的数据将通过研究期间的调查和访谈从两组参与项目中收集。这些选定项目中的研究软件质量也将在整个实验期间进行测量，以评估这些干预措施对软件质量的影响。建议的干预措施将根据该实验测试进行完善，为一系列研究软件项目遇到的当前研究软件挑战生成可靠的、基于证据和经过验证的解决方案。这项研究的结果将使研究人员能够确定哪些软件工程技术和方法最有效并且适合他们的项目（列出已知的优点或缺点），并演示如何评估和提高其研究软件的质量，所有建议都得到大力支持通过数据。