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This paper investigates the use of LLMs (Claude Sonnet 4.5 and GPT-5.2) for co-evolving textual Domain-Specific Languages (DSLs) and their instances when grammars change, addressing the limitations of traditional model-driven engineering techniques in preserving human-relevant information. The study systematically evaluates the correctness and information preservation capabilities of these LLMs across ten case languages and multiple runs, varying the scale and complexity of the grammar evolutions. Results indicate high performance on small-scale instances but a significant performance degradation with increasing instance size and grammar evolution complexity, highlighting current limitations in LLM-based co-evolution for larger and more complex DSLs.
LLMs can automatically co-evolve textual DSLs and their instances with high accuracy for small changes, but struggle to scale to larger, more complex modifications, revealing a practical limit for automated DSL maintenance.
Software languages evolve over time for reasons such as feature additions. When grammars evolve, textual instances that originally conformed to them may become outdated. While model-driven engineering provides many techniques for co-evolving models with metamodel changes, these approaches are not designed for textual DSLs and may lose human-relevant information such as layout and comments. This study systematically evaluates the potential of large language models (LLMs) for co-evolving grammars and instances of textual DSLs. Using Claude Sonnet 4.5 and GPT-5.2 across ten case languages with ten runs each, we assess both correctness and preservation of human-oriented information. Results show strong performance on small-scale cases ($\geq$94% precision and recall for instances requiring fewer than 20 modified lines), but performance degraded with scale: Claude maintains 85% recall at 40 lines, while GPT fails on the largest instances. Response time increases substantially with instance size, and grammar evolution complexity and deletion granularity affect performance more than change type. These findings clarify when LLM-based co-evolution is effective and where current limitations remain.
