The QDAcity-RE method for structural domain modeling using qualitative data analysis
Abstract The creation of domain models from qualitative input relies heavily on experience. An uncodified ad-hoc modeling process is still common and leads to poor documentation of the analysis. In this article we present a new method for domain analysis based on qualitative data analysis. The metho...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kaufmann, Andreas [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd. 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Requirements engineering - Springer London, 1996, 24(2017), 1 vom: 02. Nov., Seite 85-102 |
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| Übergeordnetes Werk: |
volume:24 ; year:2017 ; number:1 ; day:02 ; month:11 ; pages:85-102 |
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| DOI / URN: |
10.1007/s00766-017-0284-8 |
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OLC205149889X |
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| 520 | |a Abstract The creation of domain models from qualitative input relies heavily on experience. An uncodified ad-hoc modeling process is still common and leads to poor documentation of the analysis. In this article we present a new method for domain analysis based on qualitative data analysis. The method helps identify inconsistencies, ensures a high degree of completeness, and inherently provides traceability from analysis results back to stakeholder input. These traces do not have to be documented after the fact. We evaluate our approach using four exploratory studies. | ||
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10.1007/s00766-017-0284-8 doi (DE-627)OLC205149889X (DE-He213)s00766-017-0284-8-p DE-627 ger DE-627 rakwb eng 004 VZ Kaufmann, Andreas verfasserin (orcid)0000-0003-1463-3389 aut The QDAcity-RE method for structural domain modeling using qualitative data analysis 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract The creation of domain models from qualitative input relies heavily on experience. An uncodified ad-hoc modeling process is still common and leads to poor documentation of the analysis. In this article we present a new method for domain analysis based on qualitative data analysis. The method helps identify inconsistencies, ensures a high degree of completeness, and inherently provides traceability from analysis results back to stakeholder input. These traces do not have to be documented after the fact. We evaluate our approach using four exploratory studies. Domain modeling Domain model Requirements engineering Requirements elicitation Qualitative data analysis Riehle, Dirk aut Enthalten in Requirements engineering Springer London, 1996 24(2017), 1 vom: 02. Nov., Seite 85-102 (DE-627)218604068 (DE-600)1342870-6 (DE-576)05807032X 0947-3602 nnns volume:24 year:2017 number:1 day:02 month:11 pages:85-102 https://doi.org/10.1007/s00766-017-0284-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 AR 24 2017 1 02 11 85-102 |
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10.1007/s00766-017-0284-8 doi (DE-627)OLC205149889X (DE-He213)s00766-017-0284-8-p DE-627 ger DE-627 rakwb eng 004 VZ Kaufmann, Andreas verfasserin (orcid)0000-0003-1463-3389 aut The QDAcity-RE method for structural domain modeling using qualitative data analysis 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract The creation of domain models from qualitative input relies heavily on experience. An uncodified ad-hoc modeling process is still common and leads to poor documentation of the analysis. In this article we present a new method for domain analysis based on qualitative data analysis. The method helps identify inconsistencies, ensures a high degree of completeness, and inherently provides traceability from analysis results back to stakeholder input. These traces do not have to be documented after the fact. We evaluate our approach using four exploratory studies. Domain modeling Domain model Requirements engineering Requirements elicitation Qualitative data analysis Riehle, Dirk aut Enthalten in Requirements engineering Springer London, 1996 24(2017), 1 vom: 02. Nov., Seite 85-102 (DE-627)218604068 (DE-600)1342870-6 (DE-576)05807032X 0947-3602 nnns volume:24 year:2017 number:1 day:02 month:11 pages:85-102 https://doi.org/10.1007/s00766-017-0284-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 AR 24 2017 1 02 11 85-102 |
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10.1007/s00766-017-0284-8 doi (DE-627)OLC205149889X (DE-He213)s00766-017-0284-8-p DE-627 ger DE-627 rakwb eng 004 VZ Kaufmann, Andreas verfasserin (orcid)0000-0003-1463-3389 aut The QDAcity-RE method for structural domain modeling using qualitative data analysis 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract The creation of domain models from qualitative input relies heavily on experience. An uncodified ad-hoc modeling process is still common and leads to poor documentation of the analysis. In this article we present a new method for domain analysis based on qualitative data analysis. The method helps identify inconsistencies, ensures a high degree of completeness, and inherently provides traceability from analysis results back to stakeholder input. These traces do not have to be documented after the fact. We evaluate our approach using four exploratory studies. Domain modeling Domain model Requirements engineering Requirements elicitation Qualitative data analysis Riehle, Dirk aut Enthalten in Requirements engineering Springer London, 1996 24(2017), 1 vom: 02. Nov., Seite 85-102 (DE-627)218604068 (DE-600)1342870-6 (DE-576)05807032X 0947-3602 nnns volume:24 year:2017 number:1 day:02 month:11 pages:85-102 https://doi.org/10.1007/s00766-017-0284-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 AR 24 2017 1 02 11 85-102 |
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10.1007/s00766-017-0284-8 doi (DE-627)OLC205149889X (DE-He213)s00766-017-0284-8-p DE-627 ger DE-627 rakwb eng 004 VZ Kaufmann, Andreas verfasserin (orcid)0000-0003-1463-3389 aut The QDAcity-RE method for structural domain modeling using qualitative data analysis 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract The creation of domain models from qualitative input relies heavily on experience. An uncodified ad-hoc modeling process is still common and leads to poor documentation of the analysis. In this article we present a new method for domain analysis based on qualitative data analysis. The method helps identify inconsistencies, ensures a high degree of completeness, and inherently provides traceability from analysis results back to stakeholder input. These traces do not have to be documented after the fact. We evaluate our approach using four exploratory studies. Domain modeling Domain model Requirements engineering Requirements elicitation Qualitative data analysis Riehle, Dirk aut Enthalten in Requirements engineering Springer London, 1996 24(2017), 1 vom: 02. Nov., Seite 85-102 (DE-627)218604068 (DE-600)1342870-6 (DE-576)05807032X 0947-3602 nnns volume:24 year:2017 number:1 day:02 month:11 pages:85-102 https://doi.org/10.1007/s00766-017-0284-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 AR 24 2017 1 02 11 85-102 |
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Abstract The creation of domain models from qualitative input relies heavily on experience. An uncodified ad-hoc modeling process is still common and leads to poor documentation of the analysis. In this article we present a new method for domain analysis based on qualitative data analysis. The method helps identify inconsistencies, ensures a high degree of completeness, and inherently provides traceability from analysis results back to stakeholder input. These traces do not have to be documented after the fact. We evaluate our approach using four exploratory studies. © Springer-Verlag London Ltd. 2017 |
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Abstract The creation of domain models from qualitative input relies heavily on experience. An uncodified ad-hoc modeling process is still common and leads to poor documentation of the analysis. In this article we present a new method for domain analysis based on qualitative data analysis. The method helps identify inconsistencies, ensures a high degree of completeness, and inherently provides traceability from analysis results back to stakeholder input. These traces do not have to be documented after the fact. We evaluate our approach using four exploratory studies. © Springer-Verlag London Ltd. 2017 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC205149889X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502150800.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00766-017-0284-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC205149889X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00766-017-0284-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kaufmann, Andreas</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-1463-3389</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">The QDAcity-RE method for structural domain modeling using qualitative data analysis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd. 2017</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The creation of domain models from qualitative input relies heavily on experience. 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