Domain Modeling Using Qualitative Data Analysis

我们建议开发一种新的方法来确定需求工程中的领域模型，方法是采用社会科学中的定性数据分析方法来确定需求工程中的领域模型。定性数据分析(QDA)指的是从广泛的输入材料中建立理论所使用的研究方法，包括对研究参与者的采访。在这项工作中，我们将“理论构建”的过程等同于需求工程中创建领域模型的过程。当将我们的QDA方法应用于RE时，分析人员将遵循结构化的主题标记(编码)过程，在此过程中，他或她将使用共同的概念(代码)来注释和构造他或她希望分析的部分数据，然后将这些概念(代码)分层分组，形成新的构件-代码系统。代码系统在迭代过程中得到改进，直到它达到通过额外的数据收集和分析不再引入有意义的更改的地步。这是通过停止标准理论饱和度来衡量的。通过原始注释及其转换和扩展，领域模型的每一块都可以追溯到原始材料。当应用于需求工程过程中领域模型的创建时，我们的方法的好处如下：(1)它通过添加预需求-规范(Pre-RS)可跟踪性来弥合非正式涉众材料和正式领域模型之间的差距。这种可追溯性嵌入在RE过程中，并记录在新的中间构件--代码系统中。这消除了在事后创建痕迹的需要。(2)它通过(2.a)提供方法学指导，其中先前有经验的业务分析师大多必须仅依靠直觉和经验，以及(2.b)提供停止标准来确定需求引出过程何时用尽相关案例，从而改进了从利益相关者材料中导出域模型的过程。(3)它通过(3.a)确保域模型的完整性以及(3.b)确保一致性来改进域模型质量，其中先前的关键输入可能被遗漏，通过遵循恒定比较法的原则，这些益处已在我们进行的初步研究中显示出来。我们将我们的方法应用于实例项目，并使用常见的计算机辅助编码工具执行分析过程，同时在电子表格和备忘录中记录额外的元信息。我们建议开发一个工具来支持所提出的方法，并通过一系列对照实验来验证所提出的方法的好处。

We propose to develop a novel approach to determine domain models in requirements engineering (RE) by adopting qualitative data analysis methods from the social sciences for RE. Qualitative Data Analysis (QDA) refers to research methods used to build a theory from a wide array of input materials, including interviews with study participants. In this work, we equate the process of "theory building" with the process of creating a domain model during requirements engineering.While using our approach of QDA applied to RE, an analyst will follow a structured process of thematic labeling (coding), during which he or she annotates and structures parts of the data he or she wishes to analyze with common concepts (codes) which are then grouped hierarchically to form a new artifact, the code system. The code system is refined in an iterative process until it reaches the point where no meaningful changes are introduced any longer through additional gathering and analysis of data. This is measured by the stopping criterion theoretical saturation. By way of the original annotations and their transformations and extensions, every piece of the domain model can be traced back to the original materials. When applied to the creation of domain models during requirements engineering, the benefits of our approach are the following: (1) It closes the gap between the informal stakeholder material and formal domain models by adding pre-Requirements-Specification (pre-RS) traceability. This traceability is embedded in the RE process and documented in a new intermediate artifact, the code system. This eliminates the need to create traces after the fact.(2) It improves the process for deriving domain models from stakeholder materials by(2.a) providing a methodological guideline, where previously experienced business analysts mostly had to rely on intuition and experience only, and(2.b) providing a stopping criterion to determine when the requirements elicitation process exhausted the relevant cases.(3) It improves domain model quality by(3.a) ensuring completeness of domain models, where previously key input might have been missed, and(3.b) ensuring consistency, by following principles of the constant comparison method.Indications of these benefits have been shown within preliminary studies performed by us. We applied our method to example projects and performed the analysis process with common computer assisted coding tools, while documenting additional meta information in spreadsheets and memos.We propose to develop a tool to support the proposed method and validate the suggested benefits of the method through a series of controlled experiments.