Domain Modeling Using Qualitative Data Analysis

我们建议开发一种新的方法来确定领域模型的需求工程（RE）采用定性数据分析方法，从社会科学RE。定性数据分析（QDA）是指用于从广泛的输入材料中构建理论的研究方法，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。当使用我们的QDA方法应用于RE时，分析人员将遵循主题标记（编码）的结构化过程，在此期间，他或她注释和结构化他或她希望用公共概念（代码）分析的数据部分，然后将其分层分组以形成新的人工制品，即代码系统。代码系统在一个迭代过程中被细化，直到它达到一个点，即通过额外的数据收集和分析不再引入有意义的更改。这是通过停止标准理论饱和度来测量的。通过原始注释及其转换和扩展，域模型的每一部分都可以追溯到原始材料。当应用于需求工程中领域模型的创建时，我们的方法的优点如下：（1）通过增加预需求规范（pre-RS）的可追溯性，它缩小了非正式涉众材料和正式领域模型之间的差距。这种可追溯性被嵌入到RE过程中，并记录在一个新的中间工件（代码系统）中。这样就不需要在事后创建跟踪。(2)它通过以下方式改进了从涉众材料中导出领域模型的过程：（2.a）提供了一个方法指南，以前有经验的业务分析师大多只能依靠直觉和经验，以及（2.b）提供了一个停止标准来确定需求获取过程何时耗尽相关案例。(3)它通过（3.a）确保领域模型的完整性（以前的关键输入可能会被遗漏）和（3.b）通过遵循恒定比较方法的原则确保一致性来提高领域模型的质量。这些好处已经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到示例项目中，并使用常见的计算机辅助编码工具进行分析，同时在电子表格和备忘录中记录额外的Meta信息。我们建议开发一种工具来支持所提出的方法，并通过一系列对照实验验证该方法的建议益处。

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.