Automated Extraction of Grammar Optimization Rule Configurations for Metamodel-Grammar Co-evolution
Paper in proceeding, 2023

When a language evolves, meta-models and associated grammars need to be co-evolved to stay mutually consistent. Previous work has supported the automated migration of a grammar after changes of the meta-model to retain manual optimizations of the grammar, related to syntax aspects such as keywords, brackets, and component order. Yet, doing so required the manual specification of optimization rule configurations, which was laborious and error-prone. In this work, to significantly reduce the manual effort during meta-model and grammar co-evolution, we present an automated approach for extracting optimization rule configurations. The inferred configurations can be used to automatically replay optimizations on later versions of the grammar, thus leading to a fully automated migration process for the supported types of changes. We evaluated our approach on six real cases. Full automation was possible for three of them, with agreement rates between ground truth and inferred grammar between 88% and 67% for the remaining ones.

meta-models

grammars

co-evolution

Author

Weixing Zhang

University of Gothenburg

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

Regina Hebig

University of Gothenburg

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

Daniel Strüber

Software Engineering 2

University of Gothenburg

Jan-Philipp Steghöfer

University of Gothenburg

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

Proceedings of the 16th ACM SIGPLAN International Conference on Software Language Engineering

84-96
979-8-4007-0396-6 (ISBN)

16th ACM SIGPLAN International Conference on Software Language Engineering (SLE ’23)
Lisbon, Portugal,

Areas of Advance

Information and Communication Technology

Subject Categories

Computer Science

DOI

10.1145/3623476.3623525

More information

Latest update

6/26/2024