Please use this identifier to cite or link to this item: https://accedacris.ulpgc.es/handle/10553/139735
Título: Discrimination Criteria for Modeling Association, Aggregation, and Composition in UML Class Diagrams
Autores/as: Alemán-Flores, Miguel 
Clasificación UNESCO: 1203 Ciencia de los ordenadores
Palabras clave: Aggregation
Association
Class Diagrams
Composition
Modeling, et al.
Fecha de publicación: 2025
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: 19th International Conference on Computer Aided Systems Theory, EUROCAST 2024 
Resumen: A class diagram in the Unified Modeling Language (UML) is a powerful tool to model the structure of a system by means of classes, their attributes and operations, the relationships between them, and some additional elements. In this type of models, associations between classes play a crucial role and are determining in the subsequent stages of the development. UML provides three main types of associations for class diagrams in order to distinguish several scenarios: simple associations, shared aggregations, and composite aggregations. Several interpretations of what these three types of relationships mean and how they reflect usual situations in business or software modeling have been proposed, which causes some misunderstandings and makes it difficult to achieve the common interpretation which would be desirable in a unified language. This work intends to extract the key factors which help us decide what type of relationship best describes the domain we try to model. By means of a set of criteria which characterize them, the main differences and similarities are extracted, not only from a semantic point of view, but also considering the implications they have in the structure of the model, and the constraints which appear when using certain additional elements, such as association classes and self-associations. Furthermore, a series of examples covering a wide range of applications is provided to illustrate the distinction and make it possible to extract analogies.
URI: https://accedacris.ulpgc.es/handle/10553/139735
ISBN: 9783031829512
ISSN: 0302-9743
DOI: 10.1007/978-3-031-82949-9_39
Fuente: Lecture Notes in Computer Science[ISSN 0302-9743],v. 15172 LNCS, p. 443-457, (Enero 2025)
Colección:Actas de congresos
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