Enhancing Clustering Performance of Failed Test Cases during HIL Simulation: A Study on Deep Auto-Encoder Structures and Hyperparameter Tuning

Over the last decade, hardware-in-the-loop (HIL) simulation has been established as a safe, efficient, reliable, and flexible method for performing real-time simulation. Furthermore, in the automotive sector, the HIL system has been recommended in the ISO 26262 standard as a powerful platform for performing realistic simulation during system integration testing. As a result of performing HIL black-box tests, the results of executing test cases (TCs) are reported as pass/fail without determining the nature and root causes of the underlying failures. The conventional analysis process of the failed TCs relies on expert knowledge. The higher the number of failed TCs, the higher the cost of manual analysis in terms of time and effort. In light of the shortcomings of existing methodologies, this study presents a novel intelligent framework capable of analyzing failed TCs without the need for expert knowledge or code access. To this end, a convolutional auto-encoder-based deep-learning approach is proposed to extract representative features from the textual description of the failed TCs. Furthermore, k-means-based clustering is used to categorize the extracted features according to their respective failure classes. To illustrate the effectiveness and validate the performance of the proposed method, a virtual test drive with real-time HIL simulation is presented as a case study. The proposed model exhibits superior clustering performance compared to other standalone k-means algorithms, as demonstrated by the David Bouldin Index (DBI) and accuracy values of 0.5184 and 94.33%, respectively. Furthermore, the proposed model shows a significant advantage in terms of feature extraction and clustering performance compared to the current state-of-the-art fault-analysis method. The proposed approach not only supports the validation process and improves the safety and reliability of the systems but also reduces the costs of manual analysis in terms of time and effort.