###### Abstract  
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. a Article  
Anhangable  
As stated in ISO 26262, HIL simulation is an explicitly recommended test environment for verification and validation [8]. The ability to provide a safe, flexible, and reliable platform for simulating complex systems under real-time conditions has made it a widely used tool in both academic and industrial contexts [9]. In contrast to utilizing real physical systems, the controlled system is modeled and executed within the HIL simulator and linked to the actual System Under Test (SUT) via the Controller Area Network (CAN) bus. In such a structure, the functional and non-functional testing process is conducted against the requirements as a black-box testing approach without access to the source code. Following the completion of the test evaluation, a test report is automatically generated, comprising the evaluation results (pass or fail) and a description of the failed test cases (TCs) [10]. Despite the ability to automatically execute the TCs in the HIL, the evaluation process itself is constrained to merely reporting the results as either pass or fail, without any insight into the underlying nature or cause of the occurred failures [11]. Furthermore, the inspection steps to infer the category for the failure occurrence are carried out manually based on expert knowledge [12]. In the case of complex systems, a high number of failed TCs may be observed, which may subsequently increase the cost of failure analysis in terms of time and effort [13]. Furthermore, some failures may not be identified by the tester, which adds another factor of complexity to the process of analyzing failed TCs within HIL testing. In light of these considerations, the necessity for computer-aided analysis of failed tests has emerged, with the objective of providing failure efficiently debugging information. The objective of automated failure analysis is to identify the failure classes of failed TCs. Consequently, the high costs and time demands associated with the manual failure analysis process can be reduced.