Xiaoning Du

Registered user since Tue 18 Dec 2018

Name:Xiaoning Du

Dr Xiaoning Du is currently a lecturer (a.k.a. Assistant Professor) under the Department of Software System and Cybersecurity, Faculty of Information Technology. She received her PhD from Nanyang Technological University in 2020 and her bachelor’s degree from Fudan University in 2014. Xiaoning specializes in software engineering, AI, and cybersecurity. Her research has bridged the gap between the theory and practical usage of program analysis and formal methods in the evaluation of traditional and AI-assisted software systems, aiming for better quality assurance and security. Her publications appear in top-tier venues including S&P, TDSC, ICSE, FSE, ASE, NeurIPS, AAAI, and FM.

Affiliation:Monash University, Australia
Research interests:SE4AI, software analysis and testing


AISTA 2022 Committee Member in Organizing Committee within the AISTA 2022-track
APSEC 2022 Committee Member in Program Committee within the Technical Track-track
AISTA 2021 Committee Member in Program Committee within the AISTA-track
ASE 2022 Committee Member in Program Committee within the Student Research Competition-track
ASE 2021 Committee Member in Program Committee within the Artifact Evaluation-track
ICSE 2023 SCORE Co-Chair in Score within the SCORE 2023-track
SCORE Co-Chair in Organising Committee
ASE 2020 Author of MARBLE: Model-Based Robustness Analysis of Stateful Deep Learning Systems within the Research Papers-track
ICSE 2022 Author of On the Importance of Building High-quality Training Datasets for Neural Code Search within the Technical Track-track
ASE 2019 Author of A Quantitative Analysis Framework for Recurrent Neural Network within the Demonstrations-track
ICSE 2020 Author of Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty within the Technical Papers-track
ICSE 2019 Author of LEOPARD: Identifying Vulnerable Code for Vulnerability Assessment through Program Metrics within the Technical Track-track
Author of MARVEL: A Generic, Scalable and Effective Vulnerability Detection Platform within the ACM Student Research Competition-track