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Dr. Yan Xiao
肖艷 PhD Graduate (2019) 2019Now Associate Professor, School of Cyber Science and Technology (Shenzhen Campus), Sun Yat-sen University, China
Personal website ↗About
Dr. Yan Xiao is a PhD graduate in the Department of Computer Science, City University of Hong Kong.
Now Associate Professor, School of Cyber Science and Technology (Shenzhen Campus), Sun Yat-sen University, China.
Research Interests
Publications (28)
- UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems.
- Empirical Insights of Test Selection Metrics under Multiple Testing Objectives and Distribution Shifts.
- Advancing autonomous driving system testing: Demands, challenges, and future directions.
- Improving the undersampling technique by optimizing the termination condition for software defect prediction.
- Enhancing Valid Test Input Generation with Distribution Awareness for Deep Neural Networks.
- A Semisupervised Approach for Industrial Anomaly Detection via Self-Adaptive Clustering.
- On the relative value of imbalanced learning for code smell detection.
- On the Significance of Category Prediction for Code-Comment Synchronization.
- The impact of the distance metric and measure on SMOTE-based techniques in software defect prediction.
- Predicting the precise number of software defects: Are we there yet?
- Validating class integration test order generation systems with Metamorphic Testing.
- ROCT: Radius-based Class Overlap Cleaning Technique to Alleviate the Class Overlap Problem in Software Defect Prediction.
- Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction.
- Evaluating the effects of similar-class combination on class integration test order generation.
- COSTE: Complexity-based OverSampling TEchnique to alleviate the class imbalance problem in software defect prediction.
- Smart Contracts Vulnerability Auditing with Multi-semantics.
- Improving bug localization with word embedding and enhanced convolutional neural networks.
- A Heuristic Approach to Break Cycles for the Class Integration Test Order Generation.
- Machine translation-based bug localization technique for bridging lexical gap.
- Improving code readability classification using convolutional neural networks.