Paper Accepted by ICSE 2019

Our paper Adversarial Sample Detection for Deep Neural Networks through Model Mutation Testing is accepted by ICSE 2019. [arXiv] This work utilized the distinguishable sensitivity between adversarial and benign samples of a deep neural network to...

Paper Accepted by TSE

Our paper Automatically ‘Verifying’ Discrete-Time Complex Systems through Learning, Abstraction and Refinement is accepted by IEEE Transactions on Software Engineering (TSE). [early access] This work proposed a CEGAR-based learning approach to au...

Attended FLOC 2018

I attended FLOC 2018 at Oxford, UK and presented our work Towards ‘Verifying’ a Water Treatment System at FM. [slide] This work presented a case study to formally modeling and verifying the SWaT system at SUTD using probabilistic learning.

Honored to Win the ACM SIGSOFT Distinguished Paper Award at ICSE 2018

Our paper Towards Optimal Concolic Testing is honored to win the ACM SIGSOFT Distinguished Paper Award at ICSE 2018! This work proposed a formal model to analyze the optimal strategy for program concolic testing as well as a practical algorithm t...