Donghwan Shin
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| Faculty or Centre | Interdisciplinary Centre for Security, Reliability and Trust | ||||
| Department | SVV | ||||
| Postal Address |
Université du Luxembourg 29, avenue JF Kennedy L-1855 Luxembourg |
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| Campus Office | JFK Building, E01-108 | ||||
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| Telephone | (+352) 46 66 44 5465 | ||||
Donghwan Shin received his PhD degree from the Software Engineering Lab (SELab) in School of Computing, Korea Advanced Institute of Science and Technology (KAIST), in 2018. His research interests are in software testing, focusing on mutation testing, test adequacy criteria, and diversity in software testing. Donghwan joined the Software Verification and Validation group, V&V Lab, headed by Prof. Lionel Briand.
Last updated on: Monday, 17 September 2018
In press
Guidelines for Assessing the Accuracy of Log Message Template Identification Techniques; ; ;
in Proceedings of the 44th International Conference on Software Engineering (ICSE ’22) (in press)
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) (in press)
2022
PRINS: Scalable Model Inference for Component-based System Logs; ;
in Empirical Software Engineering (2022)
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) (2021, May 25)
Log-based Slicing for System-level Test Cases; ; ; ;
in Proceedings of ISSTA '21: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (2021, July)
A Theoretical Framework for Understanding the Relationship Between Log Parsing and Anomaly Detection; ; ;
in Proceedings of the 21st International Conference on Runtime Verification (2021, October)
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) (2021, July)
Can Offline Testing of Deep Neural Networks Replace Their Online Testing?; ; ;
in Empirical Software Engineering (2021), 26(5),
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) (2020, August 05)
