Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study
📰 ArXiv cs.AI
Learn how to leverage LLMs for multi-file DSL code generation in industrial settings, as demonstrated in a case study at BMW
Action Steps
- Adapt code-oriented LLMs to generate DSL artifacts
- Train LLMs on a dataset of existing DSL code
- Fine-tune LLMs for repository-scale change generation
- Test LLMs on a set of natural-language instructions
- Evaluate the performance of LLMs on multi-file DSL code generation
Who Needs to Know This
This micro-lesson is suitable for AI engineers, software engineers, and researchers working on code generation and domain-specific languages, particularly those in industrial settings who need to generate and modify project-root DSL artifacts
Key Insight
💡 LLMs can be adapted for multi-file DSL code generation, enabling efficient and accurate code creation in industrial settings
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Key Takeaways
Learn how to leverage LLMs for multi-file DSL code generation in industrial settings, as demonstrated in a case study at BMW
Full Article
Title: Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study
Abstract:
arXiv:2604.24678v1 Announce Type: cross Abstract: 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 case study at BMW that adapts code-oriented LLMs to generate and modify project-root DSL artifacts for an
Abstract:
arXiv:2604.24678v1 Announce Type: cross Abstract: 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 case study at BMW that adapts code-oriented LLMs to generate and modify project-root DSL artifacts for an
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