Multi-Agent Software Development for Automotive Model-Based Graphical Programming

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