Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Software Engineering 2021 |
| Herausgeber/-innen | Anne Koziolek, Ina Schaefer, Christoph Seidl |
| Erscheinungsort | Bonn |
| Seiten | 65-66 |
| Seitenumfang | 2 |
| ISBN (elektronisch) | 978-3-88579-704-3 |
| Publikationsstatus | Veröffentlicht - 2021 |
Publikationsreihe
| Name | GI-Edition. Proceedings |
|---|---|
| ISSN (elektronisch) | 1617-5468 |
Abstract
Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. To extend the so far statistical construction of hybrid development methods, we analyze 829 data points to investigate which context factors influence the choice of methods or practices. Using exploratory factor analysis, we derive five base clusters consisting of up to 10 methods. Logistic regression analysis then reveals which context factors have an influence on the integration of methods from these clusters in the development process. Our results indicate that only a few context factors including project/product size and target application domain significantly influence the choice. This summary refers to the paper “Determining Context Factors for Hybrid Development Methods with Trained Models”. This paper was published in the proceedings of the International Conference on Software and System Process in 2020.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
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Software Engineering 2021. Hrsg. / Anne Koziolek; Ina Schaefer; Christoph Seidl. Bonn, 2021. S. 65-66 (GI-Edition. Proceedings).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Determining Context Factors for Hybrid Development Methods with Trained Models.
AU - Klünder, Jil
AU - Karajic, Dzejlana
AU - Tell, Paolo
AU - Karras, Oliver
AU - Münkel, Christian
AU - Münch, Jürgen
AU - MacDonell, Stephen G.
AU - Hebig, Regina
AU - Kuhrmann, Marco
PY - 2021
Y1 - 2021
N2 - Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. To extend the so far statistical construction of hybrid development methods, we analyze 829 data points to investigate which context factors influence the choice of methods or practices. Using exploratory factor analysis, we derive five base clusters consisting of up to 10 methods. Logistic regression analysis then reveals which context factors have an influence on the integration of methods from these clusters in the development process. Our results indicate that only a few context factors including project/product size and target application domain significantly influence the choice. This summary refers to the paper “Determining Context Factors for Hybrid Development Methods with Trained Models”. This paper was published in the proceedings of the International Conference on Software and System Process in 2020.
AB - Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. To extend the so far statistical construction of hybrid development methods, we analyze 829 data points to investigate which context factors influence the choice of methods or practices. Using exploratory factor analysis, we derive five base clusters consisting of up to 10 methods. Logistic regression analysis then reveals which context factors have an influence on the integration of methods from these clusters in the development process. Our results indicate that only a few context factors including project/product size and target application domain significantly influence the choice. This summary refers to the paper “Determining Context Factors for Hybrid Development Methods with Trained Models”. This paper was published in the proceedings of the International Conference on Software and System Process in 2020.
KW - Hybrid Development Method
KW - Software Process
KW - Trained Models
UR - http://www.scopus.com/inward/record.url?scp=85126605301&partnerID=8YFLogxK
U2 - 10.18420/SE2021_21
DO - 10.18420/SE2021_21
M3 - Conference contribution
T3 - GI-Edition. Proceedings
SP - 65
EP - 66
BT - Software Engineering 2021
A2 - Koziolek, Anne
A2 - Schaefer, Ina
A2 - Seidl, Christoph
CY - Bonn
ER -