AI RESEARCH
Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study
arXiv CS.AI
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ArXi:2604.24678v1 Announce Type: cross Large language models (LLMs) perform strongly on general-purpose code generation, yet their applicability to enterprise domain-specific languages (DSLs) remains underexplored, especially for repository-scale change generation spanning multiple files and folder structures from a single natural-language (NL) instruction. We report an industrial at BMW that adapts code-oriented LLMs to generate and modify project-root DSL artifacts for an Xtext-based DSL that drives downstream Java/TypeScript code generation.