29 citations · 58 across the 10 of their papers we have counts for
11 papers
Formalizing UML State Machines for Automated Verification -- A Survey
Étienne André, Shuang Liu, Yang Liu +3
The Unified Modeling Language (UML) is a standard for modeling dynamic systems. UML behavioral state machines are used for modeling the dynamic behavior of object-oriented designs.…
Repairing Adversarial Texts through Perturbation
Guoliang Dong, Jingyi Wang, Jun Sun +5
It is known that neural networks are subject to attacks through adversarial perturbations, i.e., inputs which are maliciously crafted through perturbations to induce wrong predicti…
Self-Checking Deep Neural Networks in Deployment
Yan Xiao, Ivan Beschastnikh, David S. Rosenblum +4
The widespread adoption of Deep Neural Networks (DNNs) in important domains raises questions about the trustworthiness of DNN outputs. Even a highly accurate DNN will make mistakes…
N-PAT: A Nested Model-Checker
Hadrien Bride, Cheng-Hao Cai, Jin Song Dong +4
N-PAT is a new model-checking tool that supports the verification of nested-models, i.e. models whose behaviour depends on the results of verification tasks. In this paper, we desc…
Automated synthesis of local time requirement for service composition
Étienne André, Tian Huat Tan, Manman Chen +4
Service composition aims at achieving a business goal by composing existing service-based applications or components. The response time of a service is crucial especially in time c…
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
Yizhen Dong, Peixin Zhang, Jingyi Wang +7
Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs…