Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Metamodel-based Test Generation for Model Transformations: an Algorithm and a Tool
ISSRE '06 Proceedings of the 17th International Symposium on Software Reliability Engineering
Difference computation of large models
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Uniform Random Generation of Huge Metamodel Instances
ECMDA-FA '09 Proceedings of the 5th European Conference on Model Driven Architecture - Foundations and Applications
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
Generation of process using multi-objective genetic algorithm
Proceedings of the 2013 International Conference on Software and System Process
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Test models are needed to evaluate and benchmark algorithms and tools in model driven development. Most model generators randomly apply graph operations on graph representations of models. This approach leads to test models of poor quality. Some approaches do not guarantee the basic syntactic correctness of the created models. Even if so, it is almost impossible to guarantee, or even control, the creation of complex structures, e.g. a subgraph which implements an association between two classes. Such a subgraph consists of an association node, two association end nodes, and several edges, and is normally created by one user command. This paper presents the SiDiff Model Generator, which can generate models, or sets of models, which are syntactically correct, contain complex structures, and exhibit defined statistical characteristics.