Donghwan Shin

Lecturer

University of Sheffield

United Kingdom

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About

Donghwan Shin is a Lecturer at the University of Sheffield, United Kingdom. His research areas include deep learning-based anomaly detection, log-based failure prediction, and the assessment of model inference accuracy. He has contributed to various publications focusing on virtual environment models for cyber-physical systems and hazard boundaries in machine learning-enabled autonomous systems.

Articles (12)

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2024

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2022

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2022

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2021

Collaborators (1)

Lionel Briand

CANADA

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Donghwan Shin

Research Interests

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Articles (12)

Impact of log parsing on deep learning-based anomaly detection

Systematic Evaluation of Deep Learning Models for Log-based Failure Prediction

Rigorous Assessment of Model Inference Accuracy using Language Cardinality

Virtual Environment Model Generation for CPS Goal Verification using Imitation Learning

An Extensible Modeling Method Supporting Ontology-Based Scenario Specification and Domain-Specific Extension

An Extensible Modeling Method Supporting Ontology-Based Scenario Specification and Domain-Specific Extension

Identifying the Hazard Boundary of ML-Enabled Autonomous Systems Using Cooperative Coevolutionary Search

Identifying the Hazard Boundary of ML-Enabled Autonomous Systems Using Cooperative Coevolutionary Search

Summary of the Fourth International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2023)

PRINS: scalable model inference for component-based system logs

Correction to: Can Offline Testing of Deep Neural Networks Replace Their Online Testing?

Can Offline Testing of Deep Neural Networks Replace Their Online Testing?

Collaborators (1)

Lionel Briand

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