TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Klünder, Jil
A1  - Karajic, Dzejlana
A1  - Tell, Paolo
A1  - Karras, Oliver
A1  - Münkel, Christian
A1  - Münch, Jürgen
A1  - MacDonell, Stephen
A1  - Hebig, Regina
A1  - Kuhrmann, Marco
T1  - Determining context factors for hybrid development methods with trained models
T2  - ICSSP '20: Proceedings of the International Conference on Software and System Processes
N2  - Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. Every project is unique and, thus, many context factors have to be considered. Recent research took some initial steps towards statistically constructing hybrid development methods, yet, paid little attention to the peculiarities of context factors influencing method and practice selection. In this paper, we utilize exploratory factor analysis and logistic regression analysis to learn such context factors and to identify methods that are correlated with these factors. Our analysis is based on 829 data points from the HELENA dataset. We provide five base clusters of methods consisting of up to 10 methods that lay the foundation for devising hybrid development methods. The analysis of the five clusters using trained models reveals only a few context factors, e.g., project/product size and target application domain, that seem to significantly influence the selection of methods. An extended descriptive analysis of these practices in the context of the identified method clusters also suggests a consolidation of the relevant practice sets used in specific project contexts.
KW  - agile software development
KW  - software process
KW  - hybrid development method
KW  - exploratory factor analysis
KW  - logistical regression analysis
Y1  - 2020
SN  - 9781450375122
SB  - 9781450375122
U6  - https://doi.org/10.1145/3379177.3388898
DO  - https://doi.org/10.1145/3379177.3388898
SP  - 61
EP  - 70
S1  - 10
PB  - ACM
CY  - New York, NY
ER  -