# Leveraging LLMs to support co-evolution between definitions and instances of textual DSLs: A Systematic Evaluation

arXiv: https://arxiv.org/abs/2602.11904 · HTML: https://mlsanity.com/paper/2602.11904/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 75 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models](https://mlsanity.com/paper/2402.15938/) (2024)
- [A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages](https://mlsanity.com/paper/2410.03981/) (2024)
- [Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic](https://mlsanity.com/paper/2309.13339/) (2023)
- [Towards using Few-Shot Prompt Learning for Automating Model Completion](https://mlsanity.com/paper/2212.03404/) (2022)
- [On the use of Large Language Models in Model-Driven Engineering](https://mlsanity.com/paper/2410.17370/) (2024)
- [Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code](https://mlsanity.com/paper/2310.10508/) (2023)
- [On Codex Prompt Engineering for OCL Generation: An Empirical Study](https://mlsanity.com/paper/2303.16244/) (2023)
- [Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models](https://mlsanity.com/paper/2104.01642/) (2021)
- [Optimizing Retrieval Augmented Generation for Object Constraint Language](https://mlsanity.com/paper/2505.13129/) (2025)
- [Development and Evolution of Xtext-based DSLs on GitHub: An Empirical Investigation](https://mlsanity.com/paper/2501.19222/) (2025)
- [Leveraging LLMs to support co-evolution between definitions and instances of textual DSLs](https://mlsanity.com/paper/2512.06836/) (2025)
- [On the Evolution of Programming Languages](https://mlsanity.com/paper/2007.02699/) (2020)

## Built on by (most cited first, 1 of 1)

- [Leveraging LLMs for Grammar Adaptation: A Study on Metamodel-Grammar Co-Evolution](https://mlsanity.com/paper/2605.21465/) (2026)

## Abstract (arXiv)

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 (≥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.