Dr Donghwan Shin

School of Computer Science

Lecturer in Testing

Donghwan Shin headshot
Profile picture of Donghwan Shin headshot
D.Shin@sheffield.ac.uk
+44 114 222 1854

Full contact details

Dr Donghwan Shin
School of Computer Science
Regent Court (DCS)
211 Portobello
Sheffield
S1 4DP
Profile

Dr Donghwan Shin is a Lecturer in Software Testing at the Department of Computer Science, University of Sheffield since 2022. He did his BS (2006-2010), MS (2010-2012), and PhD (2012-2018) at KAIST, South Korea. This is followed by four years as a research associate (2018-2020) and research scientist (2020-2022) at SnT, University of Luxembourg.

Research interests

His research and teaching interests lie in mutation testing, testing for ML-enabled cyber-physical systems (e.g., ML-enabled automated driving systems), and log analysis (e.g., model inference and anomaly detection). He has published research papers at top venues such as ICSE, ICST, ISSTA, and MODELS and journals such as TSE, EMSE, and STVR.

Publications

Journal articles

Chapters

Conference proceedings papers

Preprints

  • Liang W, Baldivieso PR, Drummond R & Shin D (2024) Tuning the feedback controller gains is a simple way to improve autonomous driving performance, arXiv. RIS download Bibtex download
  • Li Z & Shin D (2024) Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMs. RIS download Bibtex download
  • Khan ZA, Shin D, Bianculli D & Briand L (2023) Impact of Log Parsing on Log-based Anomaly Detection. RIS download Bibtex download
  • Hadadi F, Dawes JH, Shin D, Bianculli D & Briand L (2023) Systematic Evaluation of Deep Learning Models for Failure Prediction. RIS download Bibtex download
  • Sharifi S, Shin D, Briand LC & Aschbacher N (2023) Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search, arXiv. RIS download Bibtex download
  • Clun D, Shin D, Filieri A & Bianculli D (2022) Rigorous Assessment of Model Inference Accuracy using Language Cardinality. RIS download Bibtex download
  • Haq FU, Shin D & Briand L (2022) Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems. RIS download Bibtex download
  • Baek Y-M, Cho E, Shin D & Bae D-H (2022) Literature Review to Collect Conceptual Variables of Scenario Methods for Establishing a Conceptual Scenario Framework, arXiv. RIS download Bibtex download
  • Shin Y-J, Shin D & Bae D-H (2022) Environment Imitation: Data-Driven Environment Model Generation Using Imitation Learning for Efficient CPS Goal Verification. RIS download Bibtex download
  • Shin D, Bianculli D & Briand L (2021) PRINS: Scalable Model Inference for Component-based System Logs, arXiv. RIS download Bibtex download
  • Haq FU, Shin D, Nejati S & Briand L (2021) Can Offline Testing of Deep Neural Networks Replace Their Online Testing?, arXiv. RIS download Bibtex download
  • Borg M, Abdessalem RB, Nejati S, Jegeden F-X & Shin D (2020) Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators, arXiv. RIS download Bibtex download
  • Haq FU, Shin D, Briand LC, Stifter T & Wang J (2020) Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper), arXiv. RIS download Bibtex download
  • Shin D, Bianculli D & Briand L (2020) Effective Removal of Operational Log Messages: an Application to Model Inference, arXiv. RIS download Bibtex download
  • Haq FU, Shin D, Nejati S & Briand L (2019) Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study, arXiv. RIS download Bibtex download
  • Shin D, Messaoudi S, Bianculli D, Panichella A, Briand L & Sasnauskas R (2019) Scalable Inference of System-level Models from Component Logs, arXiv. RIS download Bibtex download
  • Shin D, Yoo S, Papadakis M & Bae D-H (2017) Empirical Evaluation of Mutation-based Test Prioritization Techniques, arXiv. RIS download Bibtex download
  • Shin D & Bae D-H (2016) A Theoretical Framework for Understanding Mutation-Based Testing Methods. RIS download Bibtex download
Grants

SimpliFaiS: Simplification of Failure Scenarios for Machine Learning-enabled Autonomous Systems, UKRI, 01/01/2024 - 30/06/2026, £464,344, as PI