Systematic Evaluation of Model Comparison Algorithms using Model Generation

By: Lorenzo Addazi, Antonio Cicchetti


Model-Driven Engineering promotes the migration from code-centric to model-based software development. Systems consist of model collections integrating different concerns and perspectives, while semi-automated model transformations analyse quality attributes and generate executable code combining the information from these. Raising the abstraction level to models requires appropriate management technologies supporting the various software development activities. Among these, model comparison represents one of the most challenging tasks and plays an essential role in various modelling activities. Its hardness led researchers to propose a multitude of approaches adopting different approximation strategies and exploiting specific knowledge of the involved models. In this respect, almost no support is provided for the systematic evaluation of comparison approaches against specific scenarios and modelling practices, namely benchmarks. In this article we propose Benji, a framework for the automated generation of model comparison benchmarks. In particular, by giving a set of difference patterns and an initial model, users can generate model manipulation scenarios resulting from the application of the patterns on the model. The generation support provided by the framework obeys specific design principles that are considered as essential properties for the systematic evaluation of model comparison solutions, and are inherited from the general principles coming from evidence-based software engineering. The framework is validated through representative scenarios of model comparison benchmark generations.


Model Comparison Benchmark, Model Comparison, Model Matching, Model Differencing, Model Generation, Design-Space Exploration, Model-Driven Engineering

Cite as:

Lorenzo Addazi, Antonio Cicchetti, “Systematic Evaluation of Model Comparison Algorithms using Model Generation”, Journal of Object Technology, Volume 19, no. 2 (July 2020), pp. 11:1-22, doi:10.5381/jot.2020.19.2.a11.

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The JOT Journal   |   ISSN 1660-1769   |   DOI 10.5381/jot   |   AITO   |   Open Access   |    Contact