On using Sarkar Metrics to Evaluate the Modularity of Metamodels

As model-driven engineering (MDE) gets applied for the development of larger systems, the quality assurance of model-driven artifacts gets more important. Here, metamodels are particularly important as many other artifacts depend on them. Existing approaches to measure the modularity of metamodels have not been validated for metamodels thoroughly. In this paper, we evaluate the usage of the metrics suggested by Sarkar et al. to automatically measure the modularity of metamodels with the goal of automated quality improvements. For this, we analyze the data from a previous controlled experiment on the perception of metamodel quality with 24 participants, including both students and academic professionals. From the results, we were able to statistically disprove even a slight correlation with perceived metamodel quality.

[1]  Max E. Kramer,et al.  Extending the Palladio Component Model using Profiles and Stereotypes , 2012 .

[2]  Juri Di Rocco,et al.  Mining metrics for understanding metamodel characteristics , 2014, MiSE 2014.

[3]  Brice Morin,et al.  Kevoree Modeling Framework (KMF): Efficient modeling techniques for runtime use , 2014, ArXiv.

[4]  Chao Liu,et al.  Assessing the quality of metamodels , 2013, Frontiers of Computer Science.

[5]  Stefan Ulbrich,et al.  A Domain-Specific Language (DSL) for Integrating Neuronal Networks in Robot Control , 2015 .

[6]  Petr Hnetynka,et al.  SOFA 2.0: Balancing Advanced Features in a Hierarchical Component Model , 2006, Fourth International Conference on Software Engineering Research, Management and Applications (SERA'06).

[7]  Jan Mendling,et al.  Business Process Model and Notation , 2012, Lecture Notes in Business Information Processing.

[8]  Antonio Vallecillo,et al.  Quality Attributes for Software Metamodels , 2010 .

[9]  Max E. Kramer,et al.  An empirical study on the perception of metamodel quality , 2016, 2016 4th International Conference on Model-Driven Engineering and Software Development (MODELSWARD).

[10]  Jan Mendling,et al.  Understanding the Occurrence of Errors in Process Models Based on Metrics , 2007, OTM Conferences.

[11]  Avinash C. Kak,et al.  Metrics for Measuring the Quality of Modularization of Large-Scale Object-Oriented Software , 2008, IEEE Transactions on Software Engineering.

[12]  Jan Mendling,et al.  On a Quest for Good Process Models: The Cross-Connectivity Metric , 2008, CAiSE.

[13]  Jan Mendling,et al.  Error Metrics for Business Process Models , 2007, CAiSE Forum.

[14]  Jan Mendling,et al.  Prediction of Business Process Model Quality Based on Structural Metrics , 2010, ER.

[15]  Meir M. Lehman Programs, Cities, Students— Limits to Growth? , 1978 .

[16]  Dewayne E. Perry,et al.  Metrics and laws of software evolution-the nineties view , 1997, Proceedings Fourth International Software Metrics Symposium.

[17]  Richard F. Paige,et al.  What do Metamodels Really Look Like? , 2013, EESSMOD@MoDELS.

[18]  Reinhold Weiss,et al.  Analyzing the Complexity of Domain Model Representations , 2012, 2012 IEEE 19th International Conference and Workshops on Engineering of Computer-Based Systems.

[19]  Jan Mendling,et al.  Quality metrics for business process models , 2007 .

[20]  Stefan Ulbrich,et al.  A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks , 2016, J. Intell. Robotic Syst..