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Supporting Meta-model-based Language Evolution and Rapid Prototyping with Automated Grammar Optimization

Weixing Zhang, Jörg Holtmann, Daniel Strüber, Regina Hebig, Jan-Philipp Steghöfer

TL;DR

GrammarOptimizer automates grammar improvements for Xtext-generated grammars that arise during meta-model-driven DSL evolution, reducing manual editing during rapid prototyping and evolution. By extracting and applying a catalog of optimization rules, it can reproduce the textual syntax of expert-crafted grammars across multiple DSLs and evolution steps, sometimes requiring only small reconfigurations. The evaluation across seven DSLs and two evolution cases (EAST-ADL and QVTo) demonstrates substantial imitation capability and highlights where complex optimizations still require targeted manual tuning. The work offers a practical pathway to faster DSL evolution within a stable Eclipse/EMF/Xtext workflow, with implications for blended modeling, editor tooling, and long-term maintainability of language ecosystems.

Abstract

In model-driven engineering, developing a textual domain-specific language (DSL) involves constructing a meta-model, which defines an underlying abstract syntax, and a grammar, which defines the concrete syntax for the DSL. Language workbenches such as Xtext allow the grammar to be automatically generated from the meta-model, yet the generated grammar usually needs to be manually optimized to improve its usability. When the meta-model changes during rapid prototyping or language evolution, it can become necessary to re-generate the grammar and optimize it again, causing repeated effort and potential for errors. In this paper, we present GrammarOptimizer, an approach for optimizing generated grammars in the context of meta-model-based language evolution. To reduce the effort for language engineers during rapid prototyping and language evolution, it offers a catalog of configurable grammar optimization rules. Once configured, these rules can be automatically applied and re-applied after future evolution steps, greatly reducing redundant manual effort. In addition, some of the supported optimizations can globally change the style of concrete syntax elements, further significantly reducing the effort for manual optimizations. The grammar optimization rules were extracted from a comparison of generated and existing, expert-created grammars, based on seven available DSLs.

Supporting Meta-model-based Language Evolution and Rapid Prototyping with Automated Grammar Optimization

TL;DR

GrammarOptimizer automates grammar improvements for Xtext-generated grammars that arise during meta-model-driven DSL evolution, reducing manual editing during rapid prototyping and evolution. By extracting and applying a catalog of optimization rules, it can reproduce the textual syntax of expert-crafted grammars across multiple DSLs and evolution steps, sometimes requiring only small reconfigurations. The evaluation across seven DSLs and two evolution cases (EAST-ADL and QVTo) demonstrates substantial imitation capability and highlights where complex optimizations still require targeted manual tuning. The work offers a practical pathway to faster DSL evolution within a stable Eclipse/EMF/Xtext workflow, with implications for blended modeling, editor tooling, and long-term maintainability of language ecosystems.

Abstract

In model-driven engineering, developing a textual domain-specific language (DSL) involves constructing a meta-model, which defines an underlying abstract syntax, and a grammar, which defines the concrete syntax for the DSL. Language workbenches such as Xtext allow the grammar to be automatically generated from the meta-model, yet the generated grammar usually needs to be manually optimized to improve its usability. When the meta-model changes during rapid prototyping or language evolution, it can become necessary to re-generate the grammar and optimize it again, causing repeated effort and potential for errors. In this paper, we present GrammarOptimizer, an approach for optimizing generated grammars in the context of meta-model-based language evolution. To reduce the effort for language engineers during rapid prototyping and language evolution, it offers a catalog of configurable grammar optimization rules. Once configured, these rules can be automatically applied and re-applied after future evolution steps, greatly reducing redundant manual effort. In addition, some of the supported optimizations can globally change the style of concrete syntax elements, further significantly reducing the effort for manual optimizations. The grammar optimization rules were extracted from a comparison of generated and existing, expert-created grammars, based on seven available DSLs.
Paper Structure (72 sections, 5 figures, 5 tables)

This paper contains 72 sections, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Background: Textual DSL Engineering based on Meta-models
  3. Meta-model-based textual DSLs
  4. Xtext.
  5. Related Work
  6. Grammar Optimization
  7. Designing and Evolving Meta-model-based DSLs
  8. Other Approaches
  9. Methodology
  10. Selection of Sample DSLs
  11. Sources
  12. Concrete Grammar Reconstruction for BibTeX
  13. Meta-model Reconstruction for DOT
  14. Selected Cases
  15. Exclusion of Language Parts for Low-level Expressions
  16. ...and 57 more sections

Figures (5)

  • Figure 1: Instance of the generated grammar for EAST-ADL.
  • Figure 2: Overview of our methodology.
  • Figure 3: The class design for representing grammar rules.
  • Figure 4: Excerpt of the class diagram for optimization rules.
  • Figure 5: Exemplary Interplay of the Building Blocks of the GrammarOptimizer