Guest Editors' Introduction: Model-Driven Development
IEEE Software
The Pragmatics of Model-Driven Development
IEEE Software
Boltzmann Samplers for the Random Generation of Combinatorial Structures
Combinatorics, Probability and Computing
Software Abstractions: Logic, Language, and Analysis
Software Abstractions: Logic, Language, and Analysis
Metamodel-based Test Generation for Model Transformations: an Algorithm and a Tool
ISSRE '06 Proceedings of the 17th International Symposium on Software Reliability Engineering
MoDELS '08 Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems
Analytic Combinatorics
Generating instance models from meta models
FMOODS'06 Proceedings of the 8th IFIP WG 6.1 international conference on Formal Methods for Open Object-Based Distributed Systems
Fast and sound random generation for automated testing and benchmarking in objective Caml
Proceedings of the 2009 ACM SIGPLAN workshop on ML
Towards automated inconsistency handling in design models
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
Assessing the quality of model-comparison tools: a method and a benchmark data set
Proceedings of the 2nd International Workshop on Model Comparison in Practice
Assessing the Kodkod model finder for resolving model inconsistencies
ECMFA'11 Proceedings of the 7th European conference on Modelling foundations and applications
Generating realistic test models for model processing tools
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Badger: a regression planner to resolve design model inconsistencies
ECMFA'12 Proceedings of the 8th European conference on Modelling Foundations and Applications
Comparative analysis of data persistence technologies for large-scale models
Proceedings of the 2012 Extreme Modeling Workshop
Generation of process using multi-objective genetic algorithm
Proceedings of the 2013 International Conference on Software and System Process
Hawk: towards a scalable model indexing architecture
Proceedings of the Workshop on Scalability in Model Driven Engineering
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The size and the number of models is drastically increasing, preventing organizations from fully exploiting Model Driven Engineering benefits. Regarding this problem of scalability, some approaches claim to provide mechanisms that are adapted to numerous and huge models. The problem is that those approaches cannot be validated as it is not possible to obtain numerous and huge models and then to stress test them. In this paper, we face this problem by proposing a uniform generator of huge models. Our approach is based on the Boltzmann method, whose two main advantages are its linear complexity which makes it possible to generate huge models, and its uniformity, which guarantees that the generation has no bias.