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Abdelrahman Abdalla

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Open access 2026

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

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

Abdelrahman Abdalla, Vincent Thie, Joschka Schaub et al. · 1 citation
Review Jul 2026

Multi-Agent LLM Workflow for Regulatory-Driven Requirement Generation in Automotive Software Development

The increasing regulatory complexity in automotive development places significant pressure on engineering teams to derive complete and correct requirements. This paper presents a multi-agent-based large language model (LLM) workflow designed to support requirement extraction from technical specifications and regulatory documents in compliance with automotive requirement guidelines. The approach structures the requirement derivation process across collaborating agents that interpret specification and regulatory text, generate candidate requirements for the early engineering activities, and cross-validate their outputs to improve consistency and traceability. To evaluate the applicability of the workflow in an industrial context, we applied it to the draft Euro 7 emissions regulation. The agents produced requirements for relevant functional domains, which were subsequently reviewed by domain experts at FEV. The evaluation focused on correctness, completeness, and coverage. Results indicate that the agentic workflow can achieve high alignment with expert expectations, demonstrates robust coverage of regulatory intent, and reduces manual effort in the early requirements engineering phase. The findings highlight the potential of structured multi-agent LLM systems to accelerate compliant software development processes and to enhance the reproducibility and quality of regulatory requirement interpretation in the automotive domain.

Abdelrahman Abdalla, Lukas Schäfers, Fabian Schmidt et al. · 0 citations