Abstract
Using qualitative data analysis (QDA) to perform domain analysis and modeling has shown great promise. Yet, the evaluation of such approaches has been limited to single-case case studies. While these exploratory cases are valuable for an initial assessment, the evaluation of the efficacy of QDA to solve the suggested problems is restricted by the common single-case case study research design. Using our own method, called QDAcity-RE, as the example, we present an in-depth empirical evaluation of employing qualitative data analysis for domain modeling using a controlled experiment design. Our controlled experiment shows that the QDA-based method leads to a deeper and richer set of domain concepts discovered from the data, while also being more time efficient than the control group using a comparable non-QDA-based method with the same level of traceability.
Highlights
A domain model helps establish a common understanding of a domain among all stakeholders of a project
Strategic reading and QDAcity-requirements engineering (RE) both draw on the idea that besides creating a documentation artifact, the process of annotation improves the comprehension of the analyzed text [36, 37, 39]
We found strong evidence supporting an answer to RQ1, that QDAcity-RE creates more complete and consistent models of the domain, as compared to the method based on strategic reading, as measured through information retrieval metrics based on a baseline model
Summary
A domain model helps establish a common understanding of a domain among all stakeholders of a project. Our method documents traces between the domain model elements and their origin as a by-product of the analysis process. This constitutes a first step toward solving the pre-RS traceability challenge. The reason for choosing this method as a control is that it features a similar level of pre-RS traceability between the elements of the domain model and the underlying (source) text on which the analysis is based. The trace links between source material and model are fully tool supported [14], as is the case with QDAcity-RE Since both methods were new to all participants, we could evaluate the learning curve and the ease of use for untrained analysts.
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