A multi-agent large language model (LLM) framework tailored for automotive model-based software development that advances generative AI from isolated prompting to automation in complex development environments and sets a foundation for scalable AI-driven software pipelines in model-based systems engineering.
Abstract
The automotive industry is experiencing an unprecedented growth in software complexity driven by electrification, autonomy, and connectivity. Model-Based Design (MBD) using graphical development tools such as Simulink from MathWorks has become standard in the industry, but the translation of natural language software requirements into compliant, tested control models remains a largely manual and time-consuming task. This paper presents a multi-agent large language model (LLM) framework tailored for automotive model-based software development. Our framework orchestrates a set of specialized AI agents to cover the implementation and model-level verification phases of the software development lifecycle, including Simulink model generation, test case creation, compliance checking, test case execution, and automated refinement. To rigorously assess our approach, we introduce the Automotive Software Engineering Benchmark (ASWE-Bench), a new benchmark suite with 38 automotive software requirements categorized into data transformations, combinational logic, stateful logic with timers, and closed-loop control. Our results demonstrate a substantial improvement in generative AI performance: the rulebook-equipped single-agent baseline achieve only a 47.4% model-level pass rate, while our full multi-agent framework attains 73.7% across generated models. This work advances generative AI from isolated prompting to automation in complex development environments and sets a foundation for scalable AI-driven software pipelines in model-based systems engineering. To support reproducibility and future benchmarking, the proposed ASWE-Bench, including requirements and unit tests, is publicly available at: https://git-ce.rwth-aachen.de/mmp-rwth-aachen/aswe-bench
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.
Ran Wei, Letian Zhu, Haochi Wang et al.· 0 citations
A comprehensive overview of the existing tools and frameworks for implementing MAS in software engineering and a set of lessons learned and challenges that can help researchers and practitioners to select a suitable MAS framework according to their needs are provided.
Mariama Celi Serafim De Oliveira, M. Ibiyo, Marco Gianrusso et al.· 0 citations
Creating code specifications is a crucial measure to improve the trustworthiness of many industrial systems implemented in Rust with high security requirements. Because writing specifications requires highly specialized professionals and is time-consuming, the automatic generation of specifications, enabled by large language models (LLMs), has received increasing attention and shown promising results. However, these methods typically focus on partial specification generation (generating proofs after the contract is known) and on extracting dependencies between code modules using predefined relations. This is not suitable for real-world industrial systems where the goal is to generate complete specifications from scratch and where the complex dependencies between code modules are variable. To address this, we propose a multi-agent collaborative framework, StarVerus, to automate the verification of industrial Rust code. Specifically, StarVerus addresses the aforementioned limitations in two ways: 1) In the generation phase, it instructs the LLM to generate all specifications for a given code, and in the repair phase, it uses a cascaded two-stage process of contract alignment and proof repair to correct them; 2) In both the generation and repair phases, it utilizes a function call graph to adaptively obtain bidirectional contextual information (i.e., what it calls and what calls it) for each code module as an additional information source for the LLM. Furthermore, StarVerus introduces a planner-repairer-actor-rewriter multi-agent paradigm to further enhance the proof repair capabilities. Finally, the effectiveness of StarVerus is validated through experiments on benchmark datasets and deployment in a real operating system.
Chao Jiang, Ding Wang, Dugang Liu et al.· Proceedings of the 32nd ACM...· 0 citations
Autonomous Unmanned Aerial Vehicles (UAVs) are complex cyber-physical systems that require the coordinated integration of flight control, navigation, perception, communication, power management, and mission-level decision-making under safety, timing, and reliability constraints. However, many autonomous UAV development workflows still rely on document-centric requirements, separated architectural descriptions, and software implementation artifacts, which can lead to ambiguity, interface inconsistencies, and weak traceability during early design. This paper presents a Model-Based Systems Engineering (MBSE) design framework for the SysML-driven development of autonomous UAVs. The proposed framework uses the Systems Modeling Language (SysML) as a formal design backbone to structure UAV development across four connected layers: stakeholder requirements, functional decomposition, logical architecture, and physical/software allocation. SysML requirement diagrams, activity diagrams, block definition diagrams, internal block diagrams, state machine diagrams, and parametric diagrams are used to capture the functional, structural, behavioral, interface, and performance aspects of the UAV system. The logical architecture is then systematically mapped to a Robot Operating System 2 (ROS 2) software architecture by relating SysML blocks to ROS 2 nodes, flow ports and connectors to topics, request-response interactions to services, and goal-oriented behaviors to actions. The framework is illustrated at the design level using representative autonomous UAV mission scenarios, including autonomous take-off, waypoint navigation, hover stabilization, obstacle avoidance, return-to-home, and emergency handling. The resulting model supports requirement allocation, interface definition, subsystem responsibility assignment, and verification planning before simulation or physical deployment.
Deekshitha Angadi, Naveena Budda, Vikas Agarwal et al.· 0 citations
AutoMOOSE is an agentic software framework that converts a natural-language request into an executed, screened, and interpreted MOOSE phase-field simulation. Here, we deploy AutoMOOSE as a agentic software, complementing our prior work which focused on development of the agentic tool. We describe our software framework and architecture through Use Case and logical views of the 1+5 architectural-views model, covering its user roles, component structure, six-agent pipeline, physics plugin layer, Model Context Protocol interface, and screening/falsification/recovery loop. Our architecture separates physical falsification from automatic repair, so corrected simulations remain inspectable and must be re-admitted before acceptance. We focus on software design, extensibility, interoperability, and reuse of the AutoMoose framework for broad utilization in multiphysics materials design problems.
Sukriti Manna, Henry Chan, Subramanian K. R. S. Sankaranarayanan· 0 citations
An integrated methodology that combines variability modeling principles from SPLE with architectural modularization concepts is proposed, which bridges variability management and modular architecture design to address increasing system complexity in the automotive industry.
Fabian Goihl, Yannick Lindebauer, Richard von Esebeck et al.· Systems· 0 citations
