DSL development based on target meta-models. Using AST transformations for automating semantic analysis in a textual DSL framework

TL;DR

This paper presents a novel approach for developing textual DSLs by leveraging target meta-models and AST transformations, simplifying semantic analysis and automating parts of the text-to-model conversion process.

Contribution

It introduces a method that uses explicit AST transformations with a simple language to automate semantic analysis in textual DSL frameworks, focusing on target meta-models.

Findings

Simplifies semantic analysis in textual DSL development

Automates text-to-AST and AST-to-model transformations

Reduces manual effort in semantic analysis

Abstract

This paper describes an approach to creating textual syntax for Do- main-Specific Languages (DSL). We consider target meta-model to be the main artifact and hence to be developed first. The key idea is to represent analysis of textual syntax as a sequence of transformations. This is made by explicit opera- tions on abstract syntax trees (ATS), for which a simple language is proposed. Text-to-model transformation is divided into two parts: text-to-AST (developed by openArchitectureWare [1]) and AST-to-model (proposed by this paper). Our approach simplifies semantic analysis and helps to generate as much as possi- ble.