Improving the consistency and speed of qualitative data analysis to support software engineering researchers and requirements engineering practitioners

提高定性数据分析的一致性和速度，以支持软件工程研究人员和需求工程从业者

• 批准号: RGPIN-2021-02405
• 负责人: Barcomb, Ann
• 金额: $ 1.75万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764667
• 关键词: Improving; consistency; speed; qualitative; data

The systematic analysis of textual data through qualitative data analysis (QDA) is used by researchers addressing questions involving real-world phenomena in context. The technique has also been proposed for industry applications, where consistency, completeness and traceability of the analysis are required. For example, in requirements engineering (RE), QDA has been proposed as a way of systematically generating specifications which are complete and which don't rely on hidden, implicit knowledge possessed only by the analyst. However, because QDA is designed to thoroughly explore the problem under consideration, it results in an unwieldy number of concepts. This means that analysts must adopt a number of techniques to ensure consistency, which are time-consuming and may still fail to ensure consistency within a single analysis, or between group members working on the same analysis. QDA is time-consuming, which affects industry use. Consistency and time-cost both severely limit adoption of QDA. Software engineering researchers have explored extending computer-assisted qualitative data analysis software (CAQDAS) by using machine learning to propose annotations or categories for later analysis, based on initial analysis. However, none of these approaches has gone beyond the domain of software engineering, or achieved results which would lead to adoption of the technique. I propose to tackle consistency and time cost through the assistive use of natural language processing. The proposal focuses on two audiences: qualitative researchers and practitioners working in requirements engineering. RE is one of the fields where QDA has been proposed as a means of ensuring completeness, consistency, and pre-requirements specification traceability. The proposal includes exploratory research into other limitations and barriers for both academic and practitioner audiences. Ultimately, the usefulness of the proposed solution is evaluated in terms of the quality of the analyses, and the experiences of the users. I expect the results to exceed the limits of the exploratory studies due to an iterative process with extensive stakeholder engagement. This is part of a broader program to identify and address limitations in CAQDAS systems, and barriers to the use of QDA by industry. Beyond the scope of this proposal, I plan to seek solutions to other problems preventing QDA use, and examine the solutions in other areas of software engineering which would benefit from the rigor of QDA. Industry funding will be sought once initial results have demonstrated the usefulness of the approach. The proposed research will impact qualitative researchers and industry in Canada and beyond, by reducing the problems associated with using QDA. The research will be supported by two PhD students, two MScs student, and one BSc students. Due to the practical application of the research, HQP will be able to acquire both research skills and industry experience.

研究人员使用通过定性数据分析 (QDA) 对文本数据进行系统分析来解决涉及上下文中现实世界现象的问题。该技术还被提议用于需要分析的一致性、完整性和可追溯性的工业应用。例如，在需求工程（RE）中，QDA 被提议作为一种系统地生成完整的规范的方法，并且不依赖于仅由分析师拥有的隐藏的、隐含的知识。然而，由于 QDA 旨在彻底探索所考虑的问题，因此会产生大量难以处理的概念。这意味着分析师必须采用多种技术来确保一致性，这些技术非常耗时，并且可能仍然无法确保单个分析内或进行同一分析的小组成员之间的一致性。 QDA耗时较长，影响行业使用。一致性和时间成本都严重限制了 QDA 的采用。软件工程研究人员已经探索通过使用机器学习来扩展计算机辅助定性数据分析软件（CAQDAS），以根据初始分析提出注释或类别以供以后分析。然而，这些方法都没有超出软件工程的领域，也没有取得可以导致该技术采用的结果。我建议通过辅助使用自然语言处理来解决一致性和时间成本问题。 该提案主要针对两个受众：定性研究人员和需求工程领域的从业人员。 RE 是 QDA 被提议作为确保完整性、一致性和前置需求规范可追溯性的手段的领域之一。该提案包括对学术界和从业者受众的其他限制和障碍的探索性研究。最终，根据分析质量和用户体验来评估所提出解决方案的实用性。由于利益相关者广泛参与的迭代过程，我预计结果将超出探索性研究的限制。这是一个更广泛计划的一部分，旨在识别和解决 CAQDAS 系统的局限性以及行业使用 QDA 的障碍。除了本提案的范围之外，我计划寻求其他阻碍 QDA 使用的问题的解决方案，并研究软件工程其他领域的解决方案，这些解决方案将受益于 QDA 的严格性。一旦初步结果证明该方法的实用性，将寻求行业资助。拟议的研究将通过减少与使用 QDA 相关的问题来影响加拿大及其他地区的定性研究人员和行业。 该研究将得到两名博士生、两名硕士生和一名理学士学位生的支持。通过研究的实际应用，HQP将能够获得研究技能和行业经验。