Predicting the Performance of ATL Model Transformations

Abstract

Model transformation languages are special-purpose languages, which are designed to define transformations as comfortably as possible, i.e., often in a declarative way. Typically, developers create their transformations based on small input models which systematically cover the language of the input models. This makes it difficult for the developers to estimate how the transformations would perform for a large and diverse set of input models. Hence, developers would benefit from an approach for predicting the performance of model transformations based on just abstract characteristics of input models. Regression approaches based on machine learning lend themselves well to such predictions. However, it is currently unknown, whether and which regression approach is suitable in this context as well as how a model should be abstractly characterized for this purpose. We conducted several experiments to analyze how well different machine learning methods predict the execution time of model transformations defined in the Atlas Transformation Language (ATL) transformations for distinct sets of model characteristics. As possible methods, we have investigated linear regression, random forests and support vector regression using a radial basis function kernel. The results of our experiments show that support vector regression is the best choice in terms of usability and prediction accuracy for the model transformation modules covered in our experiments and is thus suited for a prediction approach. In addition, simple model characterizations based only on the number of model elements, the number of references, and the number of attributes are a suitable way to easily describe a model and to achieve decent prediction accuracy.