AI 中文总结
针对软件复杂性问题，提出RADIANT方法，结合MDE与多智能体LLMs用于系统开发。能从需求模型自动生成异构模型及可追溯链接，进行变更影响分析和形式验证。评估显示可提高语法有效性和可执行性，减少开发时间并可跨领域应用。
AI 中文摘要
软件复杂性一直是系统工程师面临的挑战。模型驱动工程（MDE）将模型视为一等工件来应对，但典型MDE过程涉及众多工具且产生不同系统方面的异构模型，难以进行可追溯性、维护和变更管理。我们提出RADIANT方法，将MDE与多智能体大语言模型（LLMs）结合用于基于模型的完整系统开发，尤其关注安全关键系统。从精心指定的需求模型出发，RADIANT自动生成跨工程阶段的异构模型及可执行的元素级可追溯链接，并提供精确的自动变更影响分析。生成的行为模型被翻译成CSP并通过反例驱动修复循环进行形式验证。评估发现多智能体分解能提高生成形式工件的语法有效性和可执行性，语义准确性因模型而异。一项六参与者研究表明开发时间减少了10到15倍，且该流程可转移到第二个领域。
英文摘要
Software complexity is a long-standing challenge for system engineers. Model-Driven Engineering (MDE) addresses it by treating models as first-class artefacts, but a typical MDE process spans many tools and produces heterogeneous models of different system aspects, making traceability, maintenance, and change management difficult. We propose RADIANT, an engineering methodology that combines MDE with Multi-Agent Large Language Models (LLMs) for complete model-based system development, with a focus on safety-critical systems. From a carefully specified requirement model, RADIANT automatically generates heterogeneous models across engineering phases -- a concept model, a domain-specific modelling language, a conforming system model, and a behaviour model -- together with executable, element-level traceability links, on top of which it provides exact, automated change-impact analysis. Generated behaviour models are translated into CSP and formally verified (e.g.\ for deadlock freedom and convergence) with a counterexample-driven repair loop. Evaluating RADIANT across three LLMs, we find that the multi-agent decomposition reliably improves the \emph{syntactic validity} of the generated formal artefacts over a single-agent baseline -- and their \emph{executability} where the model's code generation permits -- while gains in semantic accuracy are model-dependent. A six-participant study shows an order-of-magnitude ($10$--$15\times$) reduction in development time, and the unmodified pipeline transfers to a second domain.
