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ICSE 2023
Sun 14 - Sat 20 May 2023 Melbourne, Australia
Hongyu Zhang

Registered user since Sat 30 Dec 2017

Name:Hongyu Zhang

Hongyu Zhang is currently an associate professor at The University of Newcastle, Australia. Previously, he was a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD degree from National University of Singapore in 2003. His research is in the area of Software Engineering, in particular, software analytics, testing, maintenance, and reuse. The main theme of his research is to improve software quality and productivity by mining software data. He has published more than 160 research papers in international journals and conferences, including TSE, TOSEM, ICSE, FSE, POPL, AAAI, IJCAI, KDD, ASE, ISSTA, ICSME, ICDM, and USENIX. He received three ACM Distinguished Paper awards. He has also served as a program committee member for many software engineering conferences. More information about him can be found at: https://sites.google.com/site/hongyujohn/ .

Affiliation:The University of Newcastle
Research interests:Software analytics, testing, debugging, maintenance and reuse


AIOps 2023 Automated Fault Detection for Software Systems through Log Intelligence
MSR 2023 Committee Member in Data Showcase Committee within the Data and Tool Showcase Track-track
SVM 2023 Committee Member in Program Committee within the SVM 2023-track
ICSE 2023 Committee Member in New Ideas and Emerging Results within the NIER - New Ideas and Emerging Results-track
Log Parsing with Prompt-based Few-shot Learning
Runtime Performance Prediction for Deep Learning Models with Graph Neural Network
CoCoSoDa: Effective Contrastive Learning for Code Search
CONAN: Diagnosing Batch Failures for Cloud Systems
Session Chair of Software logging (part of Technical Track)
Template-based Neural Program Repair
Reusing Deep Neural Network Models through Model Re-engineering
TraceArk: Towards Actionable Performance Anomaly Alerting for Online Service Systems
Keeping Pace with Ever-Increasing Data: Towards Continual Learning of Code Intelligence Models
Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning
Committee Member in Industry Forum within the Industry Forum-track
Chair in Technical Briefings within the Technical Briefings-track
Tech briefings Chair in Organising Committee
An Empirical Study on Quality Issues of Deep Learning Platform
Incident-aware Duplicate Ticket Aggregation for Cloud Systems
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