Research
Research Team
Platforms
Recent Research Topics (Selection)
- Testing of Autonomous Driving Systems (Selection)
- EpiTester: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism
- Reality Bites: Assessing the Realism of Driving Scenarios with Large Language Models
- DeepCollision: Learning Configurations of Operating Environment of Autonomous Vehicles to Maximize their Collisions
- DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data
- SPECTRE: Scenario selection method based on search algorithms
- SafeVar: Search-based generation of safety-critical vehicle configurations
- DeepScenario: An Open Driving Scenario Dataset for Autonomous Driving System Testing
- Enhancing the realism of autonomous driving simulation with real-time co-simulation
- Uncertainty-aware Software Engineering (Selection)
- U-Model: Understanding Uncertainty in Cyber-Physical Systems: A Conceptual Model
- UncerTum: Uncertainty-Wise Cyber-Physical System test modeling
- MoSH: UML-based uncertainty modelling
- UncerTolve: Data-augmented model evolution
- UncerTest: Uncertainty-wise test case generation and minimization for Cyber-Physical Systems
- TM-Executor: Test Model Execution with Uncertainty
- UncerRobua: Uncertainty-Aware Robustness Assessment of Industrial Elevator Systems
- U-RUCM: Uncertainty requirements modelling
- Uncertainty-Aware Requirements Prioritisation with Search
- NIRVANA: Uncertainty-Aware Prediction Validator of Deep Learning Models
- PURE: Uncertainty Quantification for Self-Driving Cars
- ATTAIN: Leveraging Uncertainty Quantification in Digital Twins
- Digital Twin Technologies (Selection)
- Evolve the Model Universe of a System Universe
- PPT: Pretrain, Prompt, and Transfer: Evolving Digital Twins for Time-to-Event Analysis in Cyber-Physical Systems
- Model-based digital twins of medicine dispensers for healthcare IoT applications
- KDDT: Knowledge Distillation-Empowered Digital Twin for Anomaly Detection
- EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System
- LATTICE: Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems
- Simplexity testbed: A model-based digital twin testbed
- Quantum Software Engineering (Selection)
- Quantum Optimization for Software Engineering: A Survey
- When software engineering meets quantum computing
- Quantum Software Testing: Challenges, Early Achievements, and Opportunities
- Challenges and Opportunities in Quantum Software Architecture
- Q-LEAR: A Machine Learning-Based Error Mitigation Approach for Reliable Software Development on IBM’s Quantum Computers
- Quito: A Framework for Quantum Program Testing
- QuSBT (Quantum Search-Based Testing)
- QuCAT (QUantum CombinAtorial Testing)
- Muskit: A Mutation Analysis Tool for Quantum Software Testing
- QOIN: Noise-Aware Quantum Software Testing
- BootQA: Test Case Minimization (TCM) with Quantum Annealers
- Guess What Quantum Computing Can Do for Test Case Optimization
Older Research Topics (Selection)
- Model-based engineering (Selection)
- Zen-CC (configuration conformance checking)
- Zen-FIX (search-based nonconformity resolving)
- Search-based test case implantation for testing untested configurations
- Zen-DO (search-based configuration decision ordering)
- SBRM (mining configuration rules with search and ML)/
- SBCR (search-based recommendations of faulty configurations)
- SBORA (refactoring constraints with search)
- Search-based software engineering (Selection)
- Search-based test case optimization methods: STIPI, REMAP, UPMOA, CBGA-ES, CBGA-ES+, UncerTest, and UncerPrio.
- Search-based system misconfiguration correction method: Zen-FIX.
- Search-based test case implantation method for covering system configurations.
- Search-based system configuration decision ordering method: Zen-DO.
- Search-based and machine learning-based configuration rule mining method: SBRM.
- Search-based misconfiguration suggestion method: SBCR.
- Search-based software system constraint refactoring method: SBORA.
- Search-based method for uncertain requirements and their allocation prioritization.
- Basic research work on evaluating search algorithms and their quality indicators.
- Requirements Engineering (Selection)
- Zen-RUCM combines natural language processing with MBE to enable requirements modelling, automate test generation, and so forth.
- Some concepts of Zen-RUCM have been adopted by the SysML v2 roadmap2 and the UTP v2 standard.
- The methodologies and tools have been used by several international universities to teach hundreds of undergraduate and graduate students.