Achieving Extensibility through Product-Lines and Domain-Specific Languages: A Case Study

Author(s):  
Don Batory ◽  
Clay Johnson ◽  
Bob MacDonald ◽  
Dale von Heeder
2002 ◽  
Vol 11 (2) ◽  
pp. 191-214 ◽  
Author(s):  
Don Batory ◽  
Clay Johnson ◽  
Bob MacDonald ◽  
Dale von Heeder

2010 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrés Vignaga

Global Model Management (GMM) is a model-based approach for managing large sets ofinterrelated heterogeneous and complex MDE artifacts. Such artifacts are usually representedas models, however as many Domain Specific Languages have a textual concrete syntax,GMM also supports textual entities and model-to-text/text-to-model transformations whichare projectors that bridge the MDE technical space and the Grammarware technical space. Asthe transformations supported by GMM are executable artifacts, typing is critical forpreventing type errors during execution. We proposed the cGMM calculus which formalizesthe notion of typing in GMM. In this work, we extend cGMM with new types and rules forsupporting textual entities and projectors. With such an extension, those artifacts mayparticipate in transformation compositions addressing larger transformation problems. Weillustrate the new constructs in the context of an interoperability case study.


2021 ◽  
Vol 7 (12) ◽  
pp. eabc9800
Author(s):  
Ryan J. Gallagher ◽  
Jean-Gabriel Young ◽  
Brooke Foucault Welles

Core-periphery structure, the arrangement of a network into a dense core and sparse periphery, is a versatile descriptor of various social, biological, and technological networks. In practice, different core-periphery algorithms are often applied interchangeably despite the fact that they can yield inconsistent descriptions of core-periphery structure. For example, two of the most widely used algorithms, the k-cores decomposition and the classic two-block model of Borgatti and Everett, extract fundamentally different structures: The latter partitions a network into a binary hub-and-spoke layout, while the former divides it into a layered hierarchy. We introduce a core-periphery typology to clarify these differences, along with Bayesian stochastic block modeling techniques to classify networks in accordance with this typology. Empirically, we find a rich diversity of core-periphery structure among networks. Through a detailed case study, we demonstrate the importance of acknowledging this diversity and situating networks within the core-periphery typology when conducting domain-specific analyses.


2015 ◽  
Vol 50 (2) ◽  
pp. 23-34
Author(s):  
T. Stephen Strickland ◽  
Brianna M. Ren ◽  
Jeffrey S. Foster

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