Pattern-based Model-to-Model Transformation: Long Version

TL;DR

This paper introduces a high-level, pattern-based approach for model-to-model transformations using declarative constraints on triple graphs, enabling flexible and semantically-preserving transformation specifications.

Contribution

It proposes a novel method for defining transformations through declarative triple graph constraints and generates operational rules via deduction techniques, enhancing transformation expressiveness.

Findings

Develops a new pattern-based transformation specification framework.

Generates operational triple graph grammar rules from constraints.

Ensures preservation of semantics through deduction-based specialization.

Abstract

We present a new, high-level approach for the specification of model-to-model transformations based on declarative patterns. These are (atomic or composite) constraints on triple graphs declaring the allowed or forbidden relationships between source and target models. In this way, a transformation is defined by specifying a set of triple graph constraints that should be satisfied by the result of the transformation. The description of the transformation is then compiled into lower-level operational mechanisms to perform forward or backward transformations, as well as to establish mappings between two existent models. In this paper we study one of such mechanisms based on the generation of operational triple graph grammar rules. Moreover, we exploit deduction techniques at the specification level to generate more specialized constraints (preserving the specification semantics) reflecting pattern dependencies, from which additional rules can be derived. This is an extended version of the paper submitted to ICGT'08, with additional definitions and proofs.