Kaufmann A, Krause J, Harutyunyan N, Barcomb A, Riehle D (2022)
Publication Type: Journal article, Original article
Publication year: 2022
Original Authors: Andreas Kaufmann, Julia Krause, Nikolay Harutyunyan, Ann Barcomb, Dirk Riehle
Book Volume: 27
Pages Range: 31-51
Journal Issue: 1
DOI: 10.1007/s00766-021-00360-6
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.
APA:
Kaufmann, A., Krause, J., Harutyunyan, N., Barcomb, A., & Riehle, D. (2022). A validation of QDAcity-RE for domain modeling using qualitative data analysis. Requirements Engineering, 27(1), 31-51. https://dx.doi.org/10.1007/s00766-021-00360-6
MLA:
Kaufmann, Andreas, et al. "A validation of QDAcity-RE for domain modeling using qualitative data analysis." Requirements Engineering 27.1 (2022): 31-51.
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