DSL-Bench: Evaluating and Explaining LLM Code Generation Across Domain-Specific Languages
Story 4, from arXiv
DSL-Bench is a new benchmark framework that evaluates how well large language models generate code for domain-specific languages, revealing significant gaps between syntax and semantic correctness across different DSLs.
Key points
- DSL-Bench evaluates 7 LLMs across 18 domain-specific languages, finding mean syntax pass rates of 31.37% and semantic pass rates of 11.64%.
- Semantic correctness varies significantly between DSLs, with Knowledge Demand being the strongest negative factor.
- Prompt engineering methods like RAG-Refinement improve semantic correctness by an average of 16.29 percentage points.
- The study highlights the need to evaluate DSL code generation beyond syntax checks, focusing on semantic accuracy.
- DSL-Bench includes tools for analyzing and interpreting data-scarce DSLs through capability profiles and DSL-Profiler summaries.
Why it matters
As software engineers increasingly rely on LLMs for code generation, understanding how well these models perform across different DSLs is critical. DSL-Bench provides a structured way to assess and improve semantic correctness, which is essential for building reliable and maintainable systems.
Counterpoint
The study focuses on academic DSLs and may not fully represent the practical challenges of real-world DSLs used in industry.
Sources
This summary was written by a language model and may contain errors. Read the original for the full story.