Typed lambda-terms in categorical attributed graph transformation

TL;DR

This paper introduces a novel approach for attributed graph transformation using typed lambda-calculus with inductive types, enabling more expressive and efficient attribute computations within a single pushout framework.

Contribution

It presents a new method combining single pushout graph rewriting with typed lambda-calculus for attribute computation, improving expressiveness and efficiency.

Findings

The lambda-calculus based attribute computation is more expressive than Sigma-algebras.

The approach effectively handles single pushout constructions for graph rewriting.

Examples demonstrate the advantages of the proposed method.

Abstract

This paper deals with model transformation based on attributed graph rewriting. Our contribution investigates a single pushout approach for applying the rewrite rules. The computation of graph attributes is obtained through the use of typed lambda-calculus with inductive types. In this paper we present solutions to cope with single pushout construction for the graph structure and the computations functions. As this rewrite system uses inductive types, the expressiveness of attribute computations is facilitated and appears more efficient than the one based on Sigma-algebras. Some examples showing the interest of our computation approach are described in this paper.