Accelerating similarity-based model matching with subtree equivalence

Context:

Efficient version management of models in model-driven software engineering is vital for modeling tools, necessitating model matching, differencing, and merging to incorporate various model versions. Although similarity-based matching is the most general method, its computational complexity escalates at a cubic rate with the number of elements.

Objective:

This paper introduces StEqMatch, a subtree-equivalence-based approach to accelerate similarity model matching, inspired by the observation that consecutive version changes typically impact only a small portion of a model.

Methods:

StEqMatch initially decomposes a model into a series of subtrees. Rather than performing element-wise matching directly, our approach tries to find equivalent (i.e., either identical or closely similar) subtrees, representing the unchanged portion of a model, thus enabling quick pairing of elements within these subtrees. To effectively identify equivalent subtrees, this paper develops two hash functions for equality and similarity comparison of model trees.

Results:

Experiments using open-source Ecore and UML models indicate that StEqMatch is 1.27 to 22.5 times faster on average compared to the state-of-the-art model matching tool while reducing the error rates in most cases.

Conclusion:

StEqMatch combines subtree matching and element-wise matching, and can improve the efficiency and the quality of similarity-based model matching.