Year: 2026
Type: preprint
Abstract: Testing embedded software in modern vehicles is challenging due to system complexity, decentralized architectures, and strict safety and performance constraints. In this work, we present an end-to-end, deployment-aware testing pipeline for IoT-based automotive applications. The pipeline combines requirement-driven test and code generation with large language model (LLM) and vision-language model (VLM) assistance, and human-in-the-loop curation to reduce manual effort and improve consistency. Using Eclipse openDuT, it supports flexible, distributed deployment across geographically separated cyber-physical and IoT infrastructures, optimizing for node availability and cross-organizational coordination. For validation, we conduct a case study using a Child Presence Detection System (CPDS), achieving full functional requirement coverage across all 9 requirements and 100% Gherkin generation accuracy on the controlled requirement set. Distributed test execution across geographically separated ECUs via Eclipse openDuT confirms the pipeline's applicability to OEM--supplier testing workflows.
Source: arXiv (Cornell University)
Authors: Zyberaj, Denesa, Vintonyak, Roman, Hirmer, Pascal, Aiello, Marco
Institutions: Mercedes-Benz (Germany), University of Stuttgart
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7203878440
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