Randomized Model Generation for Performance Testing of Model Transformations

Mode transformation is the key to model-based software engineering. When the model transformation is applied to industrial developments, its scalability becomes an important issue, since the model to be transformed may have a large size. To test the performance of model transformations, this paper proposes a randomized approach to generating large models as test inputs. First, the paper discusses the basic requirements and constraints for performance test input generation of the model transformation. Then, the paper presents our model generation algorithm. It can generate a model having a large size randomly and correctly within a reasonable time, according to the metamodel and user-defined constraints. Finally, an evaluation is also presented. And the result shows that our approach is more suitable for generating performance test inputs compared with existing model generation approaches.