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Determining context factors for hybrid development methods with trained models

  • 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.

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Metadaten
Author of HS ReutlingenMünch, Jürgen
DOI:https://doi.org/10.1145/3379177.3388898
ISBN:9781450375122
Erschienen in:ICSSP '20: Proceedings of the International Conference on Software and System Processes
Publisher:ACM
Place of publication:New York, NY
Document Type:Conference Proceeding
Language:English
Year of Publication:2020
Tag:agile software development; exploratory factor analysis; hybrid development method; logistical regression analysis; software process
Page Number:10
First Page:61
Last Page:70
DDC classes:004 Informatik
Open Access?:Nein
Licence (English):License Logo  Lizenzbedingungen ACM