## 2 Related Work
For example, the use of a clustering approach to group failed TCs in HIL tests according to the underlying faults was proposed in [30]. In the mentioned study, the utilization of non-code-based data sources has demonstrated that the proposed techniques possess the capacity to reduce the required analysis time by 60% in comparison to traditional inspection methods. Furthermore, the adapted approach was evaluated on a real industrial dataset comprising 8743 failed tests and 1531 faults. It should be noted, however, that the study is limited in scope to a specific application class, with the objective of organizing the debugging process without identifying the failure category of the failed TCs. However, a clustering approach has developed in our study, enabling the failure classes based on log data of embedded cyber-physical system applications, i.e., automotive systems, without the need for domain-specific knowledge. Similarly, in the context of fault localization during automotive software testing in an HIL environment, [PERSON] et al. [31] presented a method to identify the root cause of faults in HIL tests. The work is centered on the idea of comparing the normal unit test to a failed integration test using memory-update information. While black-box testing without source code offers certain advantages, it is essential to devote considerable attention to the pre- and post-processing steps that are necessary for its implementation. This presents a significant challenge in the context of large-scale industrial projects. In contrast, our proposed method requires a historical dataset to extract knowledge for subsequent inference processes. As an alternative, the recorded multivariate system-level time series can be employed in lieu of the textual information provided by the generated test report as a representation of the system behavior for the purpose of fault clustering in the HIL records [32]. For example, in [33], records of HIL test execution were employed to construct an ML-based model with the capacity to identify the underlying faults of failed TCs. In this study, a time series clustering method based on the k-means algorithm was developed to group the failed runs according to their causes based on the similarity of the recorded behaviors. Consequently, the required analysis time in system-level black-box testing can be diminished by up to 80% in a domain-independent manner. Nevertheless, the accuracy attained by pure ML techniques on a dataset comprising 57 test runs is 75.85%, therefore paving the way for further investigation. In contrast, our proposed study aims to enhance the clustering process and achieve high accuracy through the introduction of an advanced automatic feature extraction method based on a DL approach. In contrast to sequential data, the descriptions of failed TCs employed in our approach serve as the basis for training the model and are less complex in terms of analysis and preprocessing. Similarly, in the context of automated test evaluations of embedded systems, [PERSON] et al. in [34] conducted a comparative study to investigate the feasibility of using ML methods to detect abnormal test runs based on the records of previous tests. In accordance with the tenets of ML-based anomaly detection, the records of preceding tests are employed in a manner that enables the identification of test runs that deviate from normal behavior. Therefore, by concentrating on the anomalous TC, the expense of superfluous manual inspections can be diminished. The case study of a drive controller illustrates the efficacy of the proposed proximity-based anomaly detection techniques in diverse configurations. Nevertheless, in our study, we concentrate on investigating diverse nonlinear feature extraction-based DL techniques utilizing various AE variants rather than relying on expert knowledge. In conclusion, it can be observed that most existing studies on the analysis of failed TCs in HIL tests still require further development in several key areas, including accuracy, generalizability, and efficiency.