DSL-Xpert 2.0: Enhancing LLM-driven code generation for domain-specific languages

Domain-specific languages (DSLs) are essential for modeling specialized concepts, offering greater fluency and efficiency than general-purpose languages. However, their adoption is often hindered by steep learning curves, limited tools, and complex implementations. While large language models (LLMs) can generate DSL code from natural language, their performance is limited in niche areas due to a lack of training on specific DSL definitions. This paper introduces DSL-Xpert 2.0, a tool that addresses these challenges by using LLMs to generate DSL code effortlessly. Integrating grammar prompting and few-shot learning ensures the effective handling of proprietary DSLs. In addition, advanced features such as automatic grammar validation, input/output correction, and integration with platforms like OpenAI, HuggingFace, and WebLLM provide robust, reliable results while simplifying workflows for novices and experts. To further demonstrate the tool’s practical value, this paper provides a running example illustrating its workflow and a complementary user survey conducted across multiple DSLs of varying complexity, following the Technology Acceptance Model (TAM), to evaluate its impact on easing the DSL learning curve. With a user-friendly and flexible design, DSL-Xpert 2.0 supports a wide range of DSL designs with minimal configuration. Its intuitive interface allows developers to focus on innovative problem-solving rather than technical complexities. Findings from the user survey confirm that DSL-Xpert 2.0 effectively reduces the learning effort required to work with DSLs and is perceived as both useful and easy to use. Additionally, this paper provides a detailed performance analysis across various LLMs, showcasing the adaptability and effectiveness of the tool. By simplifying DSL development and lowering entry barriers, DSL-Xpert 2.0 accelerates adoption and innovation, positioning itself as a valuable resource for domain-specific projects.