Abboush, MohammadKnieke, ChristophRausch , Andreas2024-12-262024-12-262025-01-07978-0-9981331-8-8a989f2a1-41f2-4636-ad0e-de1903809162https://hdl.handle.net/10125/109717In the automotive industry, according to ISO 26262, comprehensive testing is conducted to ensure software systems quality over various phases of the V-model. However, at the system integration and testing phase, a significant amount of time series data is analyzed manually based on expert knowledge to identify the nature of the failure occurring, which is a costly process in terms of time, effort, and difficulty. This study proposes a novel Machine Learning (ML)-assisted failure analysis approach for the real-time validation of automotive software systems (ASSs). Specifically, based on a representative critical faults dataset, intelligent data-driven ML-based Fault Detection and Diagnosis (FDD) models are developed, including LSTM and Denoising Autoencoder (DAE)+k-means techniques for known and unknown sensor-related faults, respectively. The novelty of these models lies in their ability to identify single and simultaneous faults in noisy conditions. To assess the efficacy of the developed model, a dataset from a virtual test drive platform with a high-fidelity entire vehicle model was employed. The evaluation outcomes illustrate the superiority of the proposed LSTM model for known faults classification in comparison to other state-of-the-art methods, with an average F1-score of 91.85%. Furthermore, the integration of DAE with k-means exhibited a high clustering performance against noise with a low MSE and DB, i.e., 0.044 and 0.68, respectively. Consequently, the proposed approach facilitates the real-time validation of the ASSs in an efficient manner, thereby enhancing the safety and reliability of engineering.10Attribution-NonCommercial-NoDerivatives 4.0 InternationalImpact of AI on Software Engineeringautomotive software systems development, deep learning, fault detection and diagnosis, fault effect analysis, hil testingConference Paper10.24251/HICSS.2025.865