Using Metrics for Assessing the Quality of ASF+SDF Model Transformations

  • Authors:

  • Marcel F. Amstel;Christian F. Lange;Mark G. Brand

  • Affiliations:

  • Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands;Federal Office for Information Technology, Cologne, Germany;Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

  • Venue:
  • ICMT '09 Proceedings of the 2nd International Conference on Theory and Practice of Model Transformations
  • Year:
  • 2009

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Abstract

Model transformations are an essential part of Model Driven Engineering and are in many ways similar to traditional software artifacts. Therefore it is necessary to define and evaluate the quality of model transformations. We propose a set of six quality attributes to evaluate the quality of model transformations. We define 27 metrics for ASF+SDF model transformations to predict the quality attributes we propose. Metrics data has been collected from six heterogeneous model transformations automatically. The quality of the same transformations has been evaluated manually by several ASF+SDF experts. We assess whether the automatically collected metrics are appropriate predictors for the quality attributes by correlating the metrics data with the expert data. Based on the measurement results, we identify a set of predicting metrics for each of the quality attributes for model transformations.