Automated Extraction of Grammar Optimization Rule Configurations for Metamodel-Grammar Co-evolution

Paper i 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.