Minxiao Li, Shuying Yan, Li Zhang et al.
Recently, Large Language Models (LLMs) have demonstrated significant potential in automating software engineering tasks. Generating software architecture designs from requirement documents is a crucial step in software development. However, there is currently a lack of functional datasets tailored for this task. To bridge this gap, we introduce R2ABench (Requirement-To-Architecture Benchmark), a novel benchmark comprising diverse real-world software projects paired with comprehensive Product Requirements Documents (PRDs) and expert-curated PlantUML reference diagrams. Furthermore, we propose a multi-dimensional, hybrid evaluation framework that assesses generated diagrams across three complementary layers: Structural Graph Metrics, Multi-dimensional Scoring, and Architecture Anti-pattern Detection. Using this framework, we conducted a comprehensive empirical study evaluating state-of-the-art models and agentic workflows. Our study shows that LLMs show strong syntactic validity and robust entity extraction but fundamentally struggle with relational reasoning, leading to structurally fragmented architectures. Code-specialized models partially alleviate this limitation, while agent frameworks introduce significant instability rather than consistent improvements. R2ABench provides a robust and standardized foundation for advancing LLM-driven software architecture generation.