| 摘要: |
| 嵌入式系统建模是基于模型的软件开发的重要组成, 体系结构分析与设计语言(architecture analysis and design language, AADL)因其形式化表达软硬件结构与交互关系的能力, 广泛用于架构设计. 大语言模型(large language model, LLM)为从自然语言需求生成架构模型提供了新路径. 然而, 现有模型在需求语义理解、AADL组件边界识别与连接关系建构等方面仍存在显著不足, 限制了其实用性与生成质量. 为解决上述问题, 提出一种面向嵌入式系统的智能建模方法——SmartGen-AADL, 整体方法基于多智能体协同机制构建, 融合语义解析、结构识别与提示增强生成等关键技术, 实现从自然语言需求到结构化AADL模型的高质量转换. 方法核心包括3个阶段: 首先, 系统通过结构化智能体完成系统架构文档中系统架构的识别与标准化需求语句的提取; 随后, 子问题智能体基于条目级分析与组件交互挖掘, 实现对需求粒度的细化与交互关系的显式建模; 最后, 构件生成智能体在语义提示中融合结构引导与基于检索增强生成(retrieval-augmented generation, RAG)的相似组件检索结果, 引导LLM生成符合AADL语法规范的组件代码. 为支撑上述流程, 构建了“条目化需求-AADL组件”知识库以及“系统架构文档-AADL架构”语义对齐数据集. 在15个嵌入式系统应用场景上的实验结果表明, 相较于仅依赖简单提示工程的方法, 所提多智能体协同建模方法在4个主流大语言模型上均展现出显著优势. 其中, 在DeepSeek-r1模型上的提升最为突出: 组件错误行占比平均降低34.37%, FBERT语义相似度平均提升6.21%, 结构匹配度提升超过20%, 人工评分整体提高约0.7分. 进一步的消融实验结果显示, 子问题识别机制增强了对建模粒度的控制能力; 系统结构树构建提供了组件组织与层级拓扑信息; 检索增强生成机制为模型提供了外部知识支撑并降低了幻觉率; 通信连接识别确保了模型的接口完备性与交互闭环, 四者协同促进了自然语言到AADL建模语言对齐与模型一致性的显著提升. |
| 关键词: 结构分析与设计语言(AADL) 需求分析 多智能体 系统工程 |
| DOI：10.13328/j.cnki.jos.007595 |
| 分类号:TP311 |
| 基金项目:国家自然科学基金(61977020); 工信部高质量发展专项(CEIEC-2024-ZM02-0067); 黑龙江省自然科学基金(LH2019F046); 黑龙江省重点研发计划(JD2023SJ21); 哈尔滨市科技计划(2022ZCZJCG019); 深圳市关键技术攻关项目(JSGG2021110892802003) |
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| SmartGen-AADL: Multi-agent-driven System Requirements Analysis and AADL Model Generation |
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GE Chu-Yan, WANG Pei-Yuan, WANG Tian-Tian, HUANG Yi-Ming, YANG Xiao-Tian
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Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
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| Abstract: |
| Modeling embedded systems is an essential component of model-based software development. The architecture analysis and design language (AADL), with its ability to formally express hardware-software structures and interaction relationships, is widely applied in system design. Large language models (LLMs) provide a new pathway for generating architecture models from natural language requirements. However, existing approaches exhibit significant limitations in requirement semantic understanding, boundary identification of AADL components, and construction of connection relationships, which constrain their practicality and the quality of generated models. To address these challenges, this study proposes an intelligent modeling approach for embedded systems, termed SmartGen-AADL. The overall framework is built upon a multi-agent collaboration mechanism, integrating key techniques such as semantic parsing, structural recognition, and prompt-enhanced generation, thus enabling high-quality transformation from natural language requirements into structured AADL models. The method consists of three core stages: (1) a structural agent identifies system architectures from system architecture documents and extracts standardized requirement statements; (2) a sub-problem agent performs item-level analysis and interaction mining to refine requirement granularity and explicitly model component interactions; (3) a component generation agent incorporates structural guidance and retrieval-augmented generation (RAG) of similar components into semantic prompts, guiding the LLM to produce component code that conforms to AADL syntax. To support this process, a knowledge base of “itemized requirements-AADL components” and a semantic alignment dataset of “system architecture documents-AADL architectures” are constructed. Experimental results on 15 embedded system application scenarios demonstrate that, compared with approaches solely relying on prompt engineering, the proposed multi-agent collaborative modeling method achieves significant improvements across four mainstream LLMs. Among them, the performance gains are most pronounced on the DeepSeek-r1 model: the component line error rate is reduced by an average of 34.37%, FBERT semantic similarity is increased by 6.21%, structural matching accuracy improves by more than 20%, and human evaluation scores rise by approximately 0.7 points. Furthermore, results from the ablation study reveal that the sub-problem identification mechanism enhances control over modeling granularity. The system structure tree contributes to component organization and hierarchical topology information. The retrieval-augmented generation mechanism supplies external knowledge support and reduces hallucination. Communication connection recognition ensures interface completeness and closed interaction loops. The synergy of these four mechanisms substantially promotes alignment between natural language requirements and the AADL modeling language, thereby improving model consistency. |
| Key words: architecture analysis and design language (AADL) requirements analysis multi-agent systems engineering |
