Handling crosscutting constraints in domain-specific modeling
Communications of the ACM
Computer
Proceedings of the 25th International Conference on Software Engineering
An end-to-end domain-driven software development framework
OOPSLA '03 Companion of the 18th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
IEEE Transactions on Software Engineering
Instant and Incremental Transformation of Models
Proceedings of the 19th IEEE international conference on Automated software engineering
Mapping EDOC to Web Services using YATL
EDOC '04 Proceedings of the Enterprise Distributed Object Computing Conference, Eighth IEEE International
ECBS '05 Proceedings of the 12th IEEE International Conference and Workshops on Engineering of Computer-Based Systems
A comprehensive model transformation approach to automated model construction and evolution
OOPSLA '05 Companion to the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
A model transformation approach to automatic model construction and evolution
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Model replication: transformations to address model scalability
Software—Practice & Experience
Model Transformation by Demonstration
MODELS '09 Proceedings of the 12th International Conference on Model Driven Engineering Languages and Systems
Model scalability using a model recording and inference engine
Proceedings of the ACM international conference companion on Object oriented programming systems languages and applications companion
Model-driven generative techniques for scalable performabality analysis of distributed systems
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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In Model Integrated Computing, it is desirable to evaluate different design alternatives as they relate to issues of scalability. A typical approach to address scalability is to create a base model that captures the key interactions of various components (i.e., the essential properties and connections among modeling entities). A collection of base models can be adorned with necessary information to characterize their replication. In current practice, replication is accomplished by scaling the base model manually. This is a time-consuming process that represents a source of error, especially when there are deep interactions between model components. As an alternative to the manual process, this paper presents the idea of a replicator, which is a model transformation that expands the number of elements from the base model and makes the correct connections among the generated modeling elements. The paper motivates the need for replicators through case studies taken from models supporting different domains.