Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering
Back-to-back (B2B) test has been introduced as a pivotal method for ensuring equivalence between model-level and implementation-level behaviour during the validation process of Automotive Software Systems (ASSs). Conventionally, the analysis of B2B execution results depends on the application of exp...
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025019711 |
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| author | Mohammad Abboush Christoph Knieke Andreas Rausch |
| author_facet | Mohammad Abboush Christoph Knieke Andreas Rausch |
| author_sort | Mohammad Abboush |
| collection | DOAJ |
| description | Back-to-back (B2B) test has been introduced as a pivotal method for ensuring equivalence between model-level and implementation-level behaviour during the validation process of Automotive Software Systems (ASSs). Conventionally, the analysis of B2B execution results depends on the application of expert knowledge and the utilisation of predetermined thresholds for the identification of failures. This approach, however, is limited in its ability to address the complexities inherent in modern systems, particularly in the presence of non-linear dynamic multivariate behaviour under noisy conditions. To address these limitations, in this study, an intelligent analysis approach is proposed for assisting the test engineers during the analysis process of the B2B test execution results. The approach is capable of automatically detecting and clustering the faults in an efficient manner considering the noisy conditions. To this end, a CNN-LSTM-based denoising autoencoder (DAE) architecture has been developed to accurately detect the faults in the test recordings based on fault-free dataset. Furthermore, an adopting density-based clustering method, i.e., DBSCAN, has been proposed to group the detected faults based on representative features extracted from DAE. The evaluation results demonstrate the superiority of the proposed approach in comparison to state-of-the-art methods in terms of performance and computational cost with F1-score 96.15%, DBI 0.159 and testing time 5.2 ms. Additionally, the experimental findings demonstrate that the proposed approach satisfy the criteria for robustness and generalisation in the presence of noise across diverse driving scenarios with MSE of 0.00312 at 10% noise. Consequently, the proposed approach has the potential to reduce the time and effort associated with traditional analysis while improving the safety and reliability of complex dynamic vehicle systems. |
| format | Article |
| id | doaj-art-34c5052e622c4cb18ed2d1499b7ffeea |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-34c5052e622c4cb18ed2d1499b7ffeea2025-08-20T03:30:19ZengElsevierResults in Engineering2590-12302025-09-012710590010.1016/j.rineng.2025.105900Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clusteringMohammad Abboush0Christoph Knieke1Andreas Rausch2Corresponding author.; Technische Universität Clausthal, Institute for Software and Systems Engineering, Arnold-Sommerfeld-Straße 1, Clausthal-Zellerfeld, 38678, Niedersachsen, GermanyTechnische Universität Clausthal, Institute for Software and Systems Engineering, Arnold-Sommerfeld-Straße 1, Clausthal-Zellerfeld, 38678, Niedersachsen, GermanyTechnische Universität Clausthal, Institute for Software and Systems Engineering, Arnold-Sommerfeld-Straße 1, Clausthal-Zellerfeld, 38678, Niedersachsen, GermanyBack-to-back (B2B) test has been introduced as a pivotal method for ensuring equivalence between model-level and implementation-level behaviour during the validation process of Automotive Software Systems (ASSs). Conventionally, the analysis of B2B execution results depends on the application of expert knowledge and the utilisation of predetermined thresholds for the identification of failures. This approach, however, is limited in its ability to address the complexities inherent in modern systems, particularly in the presence of non-linear dynamic multivariate behaviour under noisy conditions. To address these limitations, in this study, an intelligent analysis approach is proposed for assisting the test engineers during the analysis process of the B2B test execution results. The approach is capable of automatically detecting and clustering the faults in an efficient manner considering the noisy conditions. To this end, a CNN-LSTM-based denoising autoencoder (DAE) architecture has been developed to accurately detect the faults in the test recordings based on fault-free dataset. Furthermore, an adopting density-based clustering method, i.e., DBSCAN, has been proposed to group the detected faults based on representative features extracted from DAE. The evaluation results demonstrate the superiority of the proposed approach in comparison to state-of-the-art methods in terms of performance and computational cost with F1-score 96.15%, DBI 0.159 and testing time 5.2 ms. Additionally, the experimental findings demonstrate that the proposed approach satisfy the criteria for robustness and generalisation in the presence of noise across diverse driving scenarios with MSE of 0.00312 at 10% noise. Consequently, the proposed approach has the potential to reduce the time and effort associated with traditional analysis while improving the safety and reliability of complex dynamic vehicle systems.http://www.sciencedirect.com/science/article/pii/S2590123025019711HIL testingFault detection and clusteringAutomotive software systemsReal-time validationBack-to-back testingDeep denoising autoencoder |
| spellingShingle | Mohammad Abboush Christoph Knieke Andreas Rausch Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering Results in Engineering HIL testing Fault detection and clustering Automotive software systems Real-time validation Back-to-back testing Deep denoising autoencoder |
| title | Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering |
| title_full | Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering |
| title_fullStr | Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering |
| title_full_unstemmed | Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering |
| title_short | Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering |
| title_sort | intelligent back to back testing with denoising autoencoder based fault detection and dbscan clustering |
| topic | HIL testing Fault detection and clustering Automotive software systems Real-time validation Back-to-back testing Deep denoising autoencoder |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025019711 |
| work_keys_str_mv | AT mohammadabboush intelligentbacktobacktestingwithdenoisingautoencoderbasedfaultdetectionanddbscanclustering AT christophknieke intelligentbacktobacktestingwithdenoisingautoencoderbasedfaultdetectionanddbscanclustering AT andreasrausch intelligentbacktobacktestingwithdenoisingautoencoderbasedfaultdetectionanddbscanclustering |