Automatically Discovering Hidden Transformation Chaining Constraints

TL;DR

This paper introduces a static analysis method to automatically uncover hidden chaining constraints in model transformations, aiding developers in correctly chaining transformations beyond simple metamodel matching.

Contribution

It presents a novel approach for discovering detailed chaining constraints through static analysis, improving the reliability of transformation chaining in model-driven engineering.

Findings

Successfully applied to a library of endogenous transformations

Revealed hidden chaining constraints not detectable by simple metamodel matching

Enhanced understanding of transformation chaining limitations

Abstract

Model transformations operate on models conforming to precisely defined metamodels. Consequently, it often seems relatively easy to chain them: the output of a transformation may be given as input to a second one if metamodels match. However, this simple rule has some obvious limitations. For instance, a transformation may only use a subset of a metamodel. Therefore, chaining transformations appropriately requires more information. We present here an approach that automatically discovers more detailed information about actual chaining constraints by statically analyzing transformations. The objective is to provide developers who decide to chain transformations with more data on which to base their choices. This approach has been successfully applied to the case of a library of endogenous transformations. They all have the same source and target metamodel but have some hidden chaining constraints. In such a case, the simple metamodel matching rule given above does not provide any useful information.