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，以解决与现实世界现象相关的问题。这项技术还被提出用于工业应用，其中需要分析的一致性、完整性和可追溯性。例如，在需求工程(Requirements Engineering，RE)中，QDA被提出作为一种系统地生成完整的规格说明的方法，这些规格说明不依赖于隐藏的、仅由分析师拥有的隐含知识。然而，由于QDA旨在彻底探索正在考虑的问题，它导致了大量笨拙的概念。这意味着分析人员必须采用许多技术来确保一致性，这很耗时，而且仍可能无法确保单一分析内的一致性，或从事同一分析的小组成员之间的一致性。QDA很耗时，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)，以在初始分析的基础上为以后的分析提出注释或类别。然而，这些方法都没有超越软件工程的领域，或者取得了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。该提案关注两个受众：定性研究人员和在需求工程中工作的实践者。Re是QDA被提议作为确保完整性、一致性和预需求规范可追溯性的手段之一。该提案包括对学术界和实践者受众的其他限制和障碍进行探索性研究。最后，根据分析的质量和用户的体验来评估所提议的解决方案的有用性。我预计结果将超过探索性研究的极限，因为这是一个有广泛利益相关者参与的迭代过程。这是一个更广泛的计划的一部分，目的是识别和解决CAQDAS系统中的限制，以及行业使用QDA的障碍。除了这项建议的范围之外，我计划寻找其他阻止QDA使用的问题的解决方案，并研究将从QDA的严格性中受益的软件工程其他领域的解决方案。一旦初步结果证明了这种方法的有效性，就会寻求行业资金。这项拟议的研究将通过减少与使用QDA相关的问题，对加拿大及其他地区的定性研究人员和行业产生影响。这项研究将由两名博士生、两名理科硕士学生和一名理科学生支持。由于研究的实际应用，HQP将能够获得研究技能和行业经验。

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.