Jingyi Wang

Peixin Zhang

Sudipta Chattopadhyay

IEEE Robotics Autom. Lett., December, 2023

TestSGD: Interpretable Testing of Neural Networks against Subtle Group Discrimination.

[BibT_eX]

[DOI]

ACM Trans. Softw. Eng. Methodol., November, 2023

K-ST: A Formal Executable Semantics of the Structured Text Language for PLCs.

[BibT_eX]

[DOI]

Kun Wang

Xiangxiang Chen

Peng Cheng

IEEE Trans. Software Eng., October, 2023

QuoTe: Quality-oriented Testing for Deep Learning Systems.

[BibT_eX]

[DOI]

ACM Trans. Softw. Eng. Methodol., September, 2023

Defending Cyber-Physical Systems Through Reverse-Engineering-Based Memory Sanity Check.

[BibT_eX]

[DOI]

IEEE Internet Things J., May, 2023

Prompting Frameworks for Large Language Models: A Survey.

[BibT_eX]

[DOI]

CoRR, 2023

FairRec: Fairness Testing for Deep Recommender Systems.

[BibT_eX]

[DOI]

Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, 2023

DEEPJUDGE: A Testing Framework for Copyright Protection of Deep Learning Models.

[BibT_eX]

[DOI]

Proceedings of the 45th IEEE/ACM International Conference on Software Engineering: ICSE 2023 Companion Proceedings, 2023

HODOR: Shrinking Attack Surface on Node.js via System Call Limitation.

[BibT_eX]

[DOI]

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, 2023

2022

Automatic Fairness Testing of Neural Classifiers Through Adversarial Sampling.

[BibT_eX]

[DOI]

IEEE Trans. Software Eng., 2022

Towards Comprehensively Understanding the Run-time Security of Programmable Logic Controllers: A 3-year Empirical Study.

[BibT_eX]

[DOI]

CoRR, 2022

VeriFi: Towards Verifiable Federated Unlearning.

[BibT_eX]

[DOI]

CoRR, 2022

K-ST: A Formal Executable Semantics of PLC Structured Text Language.

[BibT_eX]

[DOI]

Kun Wang

Xiangxiang Chen

Peng Cheng

CoRR, 2022

Which neural network makes more explainable decisions? An approach towards measuring explainability.

[BibT_eX]

[DOI]

Mengdi Zhang

Autom. Softw. Eng., 2022

Repairing Adversarial Texts Through Perturbation.

[BibT_eX]

[DOI]

Guoliang Dong

Sudipta Chattopadhyay

Proceedings of the Theoretical Aspects of Software Engineering, 2022

Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models.

[BibT_eX]

[DOI]

Proceedings of the 43rd IEEE Symposium on Security and Privacy, 2022

NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification.

[BibT_eX]

[DOI]

Proceedings of the 44th IEEE/ACM 44th International Conference on Software Engineering, 2022

2021

Automatically 'Verifying' Discrete-Time Complex Systems through Learning, Abstraction and Refinement.

[BibT_eX]

[DOI]

IEEE Trans. Software Eng., 2021

Adversarial attacks and mitigation for anomaly detectors of cyber-physical systems.

[BibT_eX]

[DOI]

Yifan Jia

Sudipta Chattopadhyay

Yuqi Chen

Int. J. Crit. Infrastructure Prot., 2021

Better Pay Attention Whilst Fuzzing.

[BibT_eX]

[DOI]

CoRR, 2021

Fairness Testing of Deep Image Classification with Adequacy Metrics.

[BibT_eX]

[DOI]

CoRR, 2021

Improving Neural Network Verification through Spurious Region Guided Refinement.

[BibT_eX]

[DOI]

Proceedings of the Tools and Algorithms for the Construction and Analysis of Systems, 2021

Attack as defense: characterizing adversarial examples using robustness.

[BibT_eX]

[DOI]

Proceedings of the ISSTA '21: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, 2021

RobOT: Robustness-Oriented Testing for Deep Learning Systems.

[BibT_eX]

[DOI]

Proceedings of the 43rd IEEE/ACM International Conference on Software Engineering, 2021

2020

Towards Repairing Neural Networks Correctly.

[BibT_eX]

[DOI]

CoRR, 2020

Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction.

[BibT_eX]

[DOI]

Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020

White-box fairness testing through adversarial sampling.

[BibT_eX]

[DOI]

Proceedings of the ICSE '20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June, 2020

An Empirical Study on Correlation between Coverage and Robustness for Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 25th International Conference on Engineering of Complex Computer Systems, 2020

2019

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

[BibT_eX]

[DOI]

CoRR, 2019

Analyzing Recurrent Neural Network by Probabilistic Abstraction.

[BibT_eX]

[DOI]

CoRR, 2019

Adversarial sample detection for deep neural network through model mutation testing.

[BibT_eX]

[DOI]

Proceedings of the 41st International Conference on Software Engineering, 2019

2018

Learning probabilistic models for model checking: an evolutionary approach and an empirical study.

[BibT_eX]

[DOI]

Int. J. Softw. Tools Technol. Transf., 2018

Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing.

[BibT_eX]

[DOI]

CoRR, 2018

Towards optimal concolic testing.

[BibT_eX]

[DOI]

Proceedings of the 40th International Conference on Software Engineering, 2018

Towards 'Verifying' a Water Treatment System.

[BibT_eX]

[DOI]

Proceedings of the Formal Methods - 22nd International Symposium, 2018

Importance Sampling of Interval Markov Chains.

[BibT_eX]

[DOI]

Cyrille Jégourel

Proceedings of the 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2018

2017

Toward 'verifying' a Water Treatment System.

[BibT_eX]

[DOI]

Yifan Jia

CoRR, 2017

Improving Probability Estimation Through Active Probabilistic Model Learning.

[BibT_eX]

[DOI]

Proceedings of the Formal Methods and Software Engineering, 2017

Learning Likely Invariants to Explain Why a Program Fails.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Fundamental Approaches to Software Engineering, 2017

2016

Verifying Complex Systems Probabilistically through Learning, Abstraction and Refinement.

[BibT_eX]

[DOI]