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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741956
• 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.

通过定性数据分析（QDA）的文本数据的系统分析是由研究人员解决涉及现实世界的现象在上下文中的问题。该技术也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已经被提出作为一种系统地生成规范的方式，这些规范是完整的，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，它导致了大量概念。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，而且可能仍然无法确保单个分析内或从事同一分析的小组成员之间的一致性。QDA是耗时的，这影响了行业使用。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了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），以基于初始分析提出注释或类别以供后续分析。然而，这些方法都没有超出软件工程的领域，或取得的成果，这将导致采用的技术。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。 该提案侧重于两个受众：定性研究人员和从业人员在需求工程工作。RE是QDA作为确保完整性、一致性和需求前规范可追溯性的一种手段而被提出的领域之一。该提案包括对学术界和从业人员受众的其他限制和障碍进行探索性研究。最后，所提出的解决方案的有用性进行评估的质量的分析，和用户的经验。我预计，由于利益相关者广泛参与的迭代过程，结果将超过探索性研究的限制。这是一个更广泛的计划的一部分，以确定和解决CAQDAS系统的局限性，以及行业使用QDA的障碍。在这个建议的范围之外，我计划寻求其他问题的解决方案，阻止QDA的使用，并研究在软件工程的其他领域的解决方案，这将受益于QDA的严格性。一旦初步结果证明该方法的有用性，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，影响加拿大及其他地区的定性研究人员和行业。 该研究将由两名博士生，两名硕士生和一名学士生支持。由于研究的实际应用，HQP将能够获得研究技能和行业经验。