Abstract
In the automotive industry, a rigorous testing process based on ISO 26,262 is carried out at various stages of the V-model to ensure the quality of software systems. Conventional validation of embedded electronic control units (ECUs) using hardware-in- the-loop (HIL) testing is performed in the late stages using the big bang integration style, resulting in delayed feedback, lack of scalability, and insufficient fault diagnosis. Furthermore, test recording analysis is performed manually based on expert knowledge to identify the nature of the failure occurring. This, in turn, resulted in higher development costs and effort, delays fault detection, and hinders agile collaboration. To address these gaps, this article proposes a novel continuous integration (CI)-enabled HIL testing framework to facilitate continuous software development through iterative cycles. Furthermore, based on a representative critical faults dataset, intelligent data-driven ML-assisted Fault Detection and Diagnosis (FDD) models are developed, including LSTM and K-means for the diagnosis of known and unknown sensor-related faults as classification and clustering problems, respectively. The novel aspect of the robust models lies in the integration of a denoising autoencoder (DAE) for the extraction of representative features before the classification and clustering process, considering the noise conditions. The evaluation outcomes illustrate the superiority of the proposed 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 mean squared error (MSE) and Davies-Bouldin index (DBI), i.e., 0.044 and 0.68, respectively. It has been demonstrated that the proposed methodology facilitates more efficient, automated, and accurate fault analysis within the framework of automotive software validation workflows. Consequently, this approach enhances both safety and efficiency in comparison to conventional methodologies.