Author

Andreas Rausch

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Preprint Aug 2026

Knowledge-Graph-Guided Retrieval-Augmented LLMs for Explainable Root Cause Analysis in Automotive HiL Validation

Hardware-in-the-Loop validation of automotive software systems generates large multivariate time-series recordings whose manual analysis is time-consuming and often limited to anomaly detection and fault classification rather than root-cause analysis. Although deep learning methods have shown strong performance in faul...

Hamza Ouarrad, Mohammad Abboush, Andreas Rausch · 0 citations

Review Aug 2026

Sensor-Level Fault Diagnosis for Automotive Software Validation Using Large Language Models

This study examines whether open-source instruction-tuned large language models (LLMs), given a textual description of sensor behaviour, can serve as data-efficient and interpretable engines for fault detection and diagnosis inside the validation loop.

Mohammad Abboush, Hamza Ouarrad, Andreas Rausch · 0 citations

Book Open access Aug 2026

LLM Ensemble Fault Classification for Automotive HiL Validation

The results show that coordinated multi-LLM reasoning can support robust, calibrated, and engineer-interpretable fault classification for automotive HiL validation.

Hamza Ouarrad, Mohammad Abboush, Andreas Rausch · Proceedings of the ACM/IEEE... · 0 citations