Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study

Researchers have successfully applied large language models to generate code for domain-specific languages across multiple files and folders using a single natural-language instruction, as demonstrated in a case study with BMW¹. This approach adapts code-oriented large language models to tackle complex, repository-scale code generation tasks. The study explores the potential of these models to streamline development processes in enterprise settings, particularly for domain-specific languages that are often unique to individual companies. By leveraging large language models, developers can automate tedious and time-consuming coding tasks, freeing up resources for more strategic and creative work. The implications of this research extend beyond the automotive industry, as it can be applied to various sectors that rely on custom domain-specific languages. This matters to practitioners because it can significantly improve development efficiency and reduce the risk of human error in complex coding projects.