## 2 Related Work  
Over the past decade, researchers have increasingly focused their attention on software failure analysis across the entire project life cycle [22]. A number of methodologies have been put forth to address the issue of identifying the underlying causes and the nature of failed tests. Proposed studies illustrate a variety of techniques for applying ML to identify the failure classes of failed TCs. This section presents a concise overview of recent research in the field of failure analysis during software system testing across a range of applications. Furthermore, we examine the principal limitations and contributions of the existing literature, elucidating the distinctions between our proposed study and other related works. ML-based clustering of test execution results shows remarkable achievements have been performed based on textual or time series datasets. To illustrate, [PERSON] et al. in [23] have employed word-textual data, specifically code comments that offer valuable insights into failures, to develop an approach with an implemented tool called FailureDoc. The objective of this approach is to explain failed tests, therefore offering valuable aid to developers engaged in troubleshooting and fault diagnosis. Although the conducted study demonstrated strong scalability, the process can only be derived with code comment access, which limits its applicability in black-box testing. In a similar context but pertaining to black-box testing, an automated text clustering approach was put forth in [24] with the aim of identifying the underlying causes of test failure. To this end, ML techniques, specifically Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for detection and Naive-Bayes/ Support vector machine (SVM) for classification, are employed based on the test failure messages. To train the model, three real-world datasets were employed, comprising 115, 143, and 63 failed TCs, respectively. The results demonstrate the superiority of the proposed method in terms of performance compared to several baselines. The primary limitation of the conducted study is that the training process relies on labeled datasets, which presents a significant challenge in real-world applications within the automotive industry. In contrast, our proposed method employs an unsupervised DL approach to construct a failure analysis model using unlabeled data. To address the issue of the unavailability of ground truth categories in the training data, [PERSON] et al. in [25] employed the information obtained through interviews with test engineers to extract meaningful features from the log data. Based on the collected expert knowledge, two ML algorithms were explored as classification and clustering problems with the objective of identifying the causes of failed tests in agile software testing environments. In particular, the authors employed a combination of AE and k-meansclustering, as well as Gaussian Mixture Models (GMMs) for clustering, while Artificial Neural Network (ANN) was utilized for classification purposes. The findings indicate that the classification model is applicable to the task of failure analysis task, with an accuracy of 0.880%. However, further investigation into other DL-based methods for enhancing the performance in terms of failed TC clustering is still necessary. A further distinction pertains to the level at which the tests are targeted. In the aforementioned study, the time constraints of testing were not taken into account, as the focus was on the log data of non-real-time tests. In contrast, the target test level in our study is real-time system-level HIL testing. In recent times, significant strides have been made in the automotive sector towards the utilization of real-time HIL simulation for the verification and validation of control strategies [26; 27; 28]. Concurrently, a number of studies have been proposed with the objective of automating the inspection process for HIL test results, eliminating the need for human intervention [29].