###### Abstract
Furthermore, the developed system's corrective process can be effectively implemented when the developer is provided with debugging information about the investigated failure. To address the shortcomings of a knowledge-based approach for failure analysis, a data-driven Machine Learning (ML) approach has been introduced. Instead of using expert knowledge, this approach involves extracting knowledge from historical data [14]. Consequently, in recent years, the advancement of ML-based models has been extensively investigated as a tool for the software development life cycle across various phases, including software requirements analysis, architecture design, implementation, quality assurance, and maintenance [15; 16]. Clustering methods have been utilized to extract latent patterns within the data and categorize them into multiple clusters as an unsupervised learning approach [17]. In light of this capability, clustering approaches have been employed in software testing with the objective of reducing the time and effort required for analysis by grouping similar failed test runs. Some of the studies focus on parallel debugging [18; 19], while others investigate the use of clustering for failed TCs [20]. Alternatively, some studies aim to detect the outcome of test runs (pass or fail) with the aim of creating an automated test oracle [21]. However, the studies conducted have required the accessibility of the source code in the white-box state, which is not present in HIL black-box testing, where the source code is not accessible. Furthermore, the efficacy of pure ML-based clustering techniques in terms of accuracy remains a subject for further investigation. Accordingly, in this study, we proposed a novel Deep-Learning (DL)-based framework combining One-Dimensional Convolutional Neural Network (1 dCNN)-based Auto-Encoder (AE) with k-means clustering for failure analysis of failed TCs of HIL tests as a clustering problem. The objective of the proposed framework is to determine the failure category of failed TCs based on text log data. To demonstrate the effectiveness of the proposed framework, experimental data from a real-time virtual test drive platform comprising a HIL simulator and a real ECU are employed. To the best of our knowledge, our study is the first to investigate the feasibility of using DL methods for a short text clustering problem to support failure analysis during real-time HIL testing. The main contributions of the proposed study can be summarized as follows:* A novel failure analysis framework has been proposed for identifying the failure classes of the failed TCs without expert knowledge and code access;
* To improve the clustering process, a novel feature extraction model based on deep convolutional auto-encoder has been developed;
* The effect of various AE structures on the clustering performance in terms of accuracy and David Bouldin Index (DBI) has been investigated;
* The effect of the hyperparameters tuning of the developed DL models on the clustering performance has been investigated and discussed;
* Finally, to demonstrate the applicability of the developed model, a high-fidelity entire vehicle model is used with a virtual test drive platform, including a real-time simulator and ECU. The remainder of the paper is organized as follows. Section 2 provides an overview of related work, highlighting the main differences between the proposed study and previous research. Subsequently, Section 3 delineates the architectural framework of the proposed approach. Section 4 presents a case study to validate the proposed framework, including the setup and implementation steps. Section 5 discusses the evaluation of the proposed model's results. Finally, Section 6 outlines the conclusions and future directions for research.