Donghwan Shin

Donghwan Shin is a Lecturer (Assistant Professor in the American system) at the School of Computer Science, University of Sheffield, United Kingdom. His academic career spans software engineering research with a focus on testing methodologies for modern software systems.

Dr. Shin received his PhD under the supervision of Prof. Doo-Hwan Bae at Korea Advanced Institute of Science and Technology (KAIST), South Korea. He holds both a Bachelor's and Master's degree in Computer Science from KAIST. Prior to his current position, he spent four years as a research associate/scientist at the SVV (Software Verification and Validation) group, led by Prof. Lionel Briand, at the Interdisciplinary Centre for ICT Security, Reliability, and Trust (SnT) of the University of Luxembourg.

Dr. Shin's research focuses on mutation testing, testing for ML-enabled cyber-physical systems (particularly ML-enabled automated driving systems), and log analysis (including model inference and anomaly detection). His work bridges theoretical foundations with practical applications in software quality assurance. He has developed innovative approaches for testing extended reality applications, autonomous driving systems, and deep neural network components. His research often combines traditional software testing techniques with machine learning and optimization algorithms to address the challenges of modern software systems.

Analysis of Dr. Shin's recent publications reveals a strong focus on testing methodologies for AI-enabled and safety-critical systems. His work spans multiple dimensions including mutation testing advancements, log analysis techniques, and specialized testing approaches for autonomous systems. A notable trend is his increasing focus on testing extended reality applications and the application of many-objective optimization techniques to improve test generation efficiency. His research demonstrates a consistent pattern of addressing practical challenges in software testing through rigorous empirical evaluation and innovative algorithmic approaches.

• ACM SIGSOFT Distinguished Reviewer Award (ISSTA 2025)
• Distinguished Paper Award (EASE 2025)
• Member of EPSRC Peer Review College (2025)

Dr. Shin has been actively involved in numerous research collaborations, including a recent project launched in July 2025 with the KAIST Web Engineering Lab on testing and debugging of autonomous driving system-of-systems. His service to the academic community includes committee roles in major software engineering conferences such as ASE, ICSE, ISSTA, and ICST. He has served on program committees, tutorial committees, and as an organizer for specialized workshops including DeepTest and Mutation testing events. His research has been supported by various grants that enable his work on testing methodologies for complex software systems.

Dr. Shin maintains active collaborations with research groups at KAIST and the University of Luxembourg, where he previously worked. His current research focuses on extending testing methodologies to address emerging challenges in extended reality applications, autonomous systems, and large language models. He leads a research group at the University of Sheffield that investigates innovative approaches to software quality assurance in the context of modern software architectures.