  | CECI, M., SANNIER, N., ABUALHAIJA, S., SHIN, D., BIANCULLI, D., & HALLING, M. (2024). Toward Automated Compliance Checking of Fund Activities Using Runtime Verification Techniques. In Proceedings of the 1st Workshop on Software Engineering Challenges in Financial Firms (FinanSE 2024), co-located with ICSE 2024 (pp. 19-20). ACM. doi:10.1145/3643665.3648045  Peer reviewed |
 | UL HAQ, F., SHIN, D., & BRIAND, L. (2023). Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems. In 45th International Conference on Software Engineering (ICSE ’23). New York, NY, United States: ACM. doi:10.1109/ICSE48619.2023.00155 Peer reviewed |
 | KHAN, Z. A., SHIN, D., BIANCULLI, D., & BRIAND, L. (2022). Guidelines for Assessing the Accuracy of Log Message Template Identification Techniques. In Proceedings of the 44th International Conference on Software Engineering (ICSE ’22) (pp. 1095-1106). New York, NY, United States: ACM. doi:10.1145/3510003.3510101 Peer reviewed |
 | UL HAQ, F., SHIN, D., & BRIAND, L. (2022). Efficient Online Testing for DNN-Enabled Systems using Surrogate-Assisted and Many-Objective Optimization. In Proceedings of the 44th International Conference on Software Engineering (ICSE ’22) (pp. 811-822). New York, NY, United States: ACM. doi:10.1145/3510003.3510188 Peer reviewed |
 | SHIN, D., BIANCULLI, D., & BRIAND, L. (2022). PRINS: Scalable Model Inference for Component-based System Logs. Empirical Software Engineering. doi:10.1007/s10664-021-10111-4 Peer Reviewed verified by ORBi |
 | SHIN, D., KHAN, Z. A., BIANCULLI, D., & BRIAND, L. (2021). A Theoretical Framework for Understanding the Relationship Between Log Parsing and Anomaly Detection. In Proceedings of the 21st International Conference on Runtime Verification (pp. 277-287). Springer. doi:10.1007/978-3-030-88494-9_16 Peer reviewed |
 | UL HAQ, F., SHIN, D., NEJATI, S., & BRIAND, L. (05 July 2021). Can Offline Testing of Deep Neural Networks Replace Their Online Testing? Empirical Software Engineering, 26 (5). doi:10.1007/s10664-021-09982-4 Peer Reviewed verified by ORBi |
 | UL HAQ, F., SHIN, D., BRIAND, L., Stifter, T., & Wang, J. (2021). Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper). In 2021 ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) (pp. 91-102). Peer reviewed |
 | MESSAOUDI, S., SHIN, D., Panichella, A., BIANCULLI, D., & BRIAND, L. (2021). Log-based Slicing for System-level Test Cases. In Proceedings of ISSTA '21: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 517-528). doi:10.1145/3460319.3464824 Peer reviewed |
 | Borg, M., BEN ABDESSALEM (HELALI), R., NEJATI, S., François-Xavier, J., & SHIN, D. (2021). Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators. In 2021 IEEE 14th International Conference on Software Testing, Validation and Verification (ICST) (pp. 383-393). doi:10.1109/ICST49551.2021.00050 Peer reviewed |
 | UL HAQ, F., SHIN, D., NEJATI, S., & BRIAND, L. (2020). Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study. In 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST). IEEE. doi:10.1109/ICST46399.2020.00019 Peer reviewed |
SHIN, D., BIANCULLI, D., & BRIAND, L. (2020). Effective Removal of Operational Log Messages: an Application to Model Inference. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/57625. |