Jingyi Wang

According to our database1, Jingyi Wang authored at least 21 papers between 2016 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.



In proceedings 
PhD thesis 


Online presence:

On csauthors.net:


Automatically 'Verifying' Discrete-Time Complex Systems through Learning, Abstraction and Refinement.
IEEE Trans. Software Eng., 2021

Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems.
CoRR, 2021

Improving Neural Network Verification through Spurious Region Guided Refinement.
Proceedings of the Tools and Algorithms for the Construction and Analysis of Systems, 2021

Towards Repairing Neural Networks Correctly.
CoRR, 2020

Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction.
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020

White-box fairness testing through adversarial sampling.
Proceedings of the ICSE '20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June, 2020

There is Limited Correlation between Coverage and Robustness for Deep Neural Networks.
CoRR, 2019

Analyzing Recurrent Neural Network by Probabilistic Abstraction.
CoRR, 2019

Adversarial sample detection for deep neural network through model mutation testing.
Proceedings of the 41st International Conference on Software Engineering, 2019

Learning probabilistic models for model checking: an evolutionary approach and an empirical study.
Int. J. Softw. Tools Technol. Transf., 2018

Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing.
CoRR, 2018

Towards optimal concolic testing.
Proceedings of the 40th International Conference on Software Engineering, 2018

Towards 'Verifying' a Water Treatment System.
Proceedings of the Formal Methods - 22nd International Symposium, 2018

Importance Sampling of Interval Markov Chains.
Proceedings of the 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2018

Toward 'verifying' a Water Treatment System.
CoRR, 2017

Improving Probability Estimation Through Active Probabilistic Model Learning.
Proceedings of the Formal Methods and Software Engineering, 2017

Learning Likely Invariants to Explain Why a Program Fails.
Proceedings of the 22nd International Conference on Engineering of Complex Computer Systems, 2017

Should We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study.
Proceedings of the Fundamental Approaches to Software Engineering, 2017

Verifying Complex Systems Probabilistically through Learning, Abstraction and Refinement.
CoRR, 2016

Service Adaptation with Probabilistic Partial Models.
Proceedings of the Formal Methods and Software Engineering, 2016

Towards Concolic Testing for Hybrid Systems.
Proceedings of the FM 2016: Formal Methods, 2016