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

研究人员通过定性数据分析（QDA）对文本数据进行系统分析，以解决涉及现实世界现象的问题。该技术也被建议用于工业应用，其中一致性、完整性和可追溯性分析是必需的。例如，在需求工程（RE）中，QDA被认为是一种系统地生成规范的方法，这些规范是完整的，并且不依赖于仅由分析人员拥有的隐藏的、隐含的知识。然而，由于QDA的设计目的是彻底探索所考虑的问题，因此它产生了大量的概念。这意味着分析人员必须采用许多技术来确保一致性，这是耗时的，并且可能仍然无法确保单个分析中的一致性，或者在处理相同分析的组成员之间的一致性。QDA耗时，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了扩展计算机辅助定性数据分析软件（CAQDAS），通过使用机器学习在初始分析的基础上提出注释或类别，以供以后分析。然而，这些方法都没有超出软件工程的领域，也没有取得导致采用该技术的结果。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。该建议主要针对两个受众：从事需求工程的定性研究人员和实践者。RE是QDA作为确保完整性、一致性和预需求规范可追溯性的手段而被提出的领域之一。该提案包括对学术和从业者受众的其他限制和障碍的探索性研究。最后，根据分析的质量和用户的经验来评估所建议的解决方案的有用性。我希望结果能够超越探索性研究的限制，因为这是一个具有广泛利益相关者参与的迭代过程。这是一个更广泛的项目的一部分，以确定和解决CAQDAS系统的局限性，以及QDA在工业中使用的障碍。在这个提案的范围之外，我计划寻求其他阻碍QDA使用的问题的解决方案，并研究其他软件工程领域的解决方案，这些解决方案将受益于QDA的严谨性。一旦初步结果证明了这种方法的有效性，就将寻求工业界的资助。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、两名理学硕士和一名理学士支持。由于研究的实际应用，HQP将能够获得研究技能和行业经验。

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.