| A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability X Huang, D Kroening, W Ruan, J Sharp, Y Sun, E Thamo, M Wu Computer Science Review, 2020 | 721 | 2020 |
| Concolic testing for deep neural networks Y Sun, M Wu, W Ruan, X Huang, M Kwiatkowska, D Kroening Proceedings of the 33rd ACM/IEEE International Conference on Automated …, 2018 | 383 | 2018 |
| Testing deep neural networks Y Sun, X Huang, D Kroening, J Sharp, M Hill, R Ashmore arXiv preprint arXiv:1803.04792, 2018 | 359 | 2018 |
| Structural test coverage criteria for deep neural networks Y Sun, X Huang, D Kroening, J Sharp, M Hill, R Ashmore ACM Transactions on Embedded Computing Systems (TECS) 18 (5s), 1-23, 2019 | 162 | 2019 |
| Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models J Chen, J Wang, T Peng, Y Sun, P Cheng, S Ji, X Ma, B Li, D Song IEEE S&P, 2021 | 132 | 2021 |
| Verifi: Towards verifiable federated unlearning X Gao, X Ma, J Wang, Y Sun, B Li, S Ji, P Cheng, J Chen IEEE Transactions on Dependable and Secure Computing 21 (6), 5720-5736, 2024 | 124 | 2024 |
| Global robustness evaluation of deep neural networks with provable guarantees for the hamming distance W Ruan, M Wu, Y Sun, X Huang, D Kroening, M Kwiatkowska International Joint Conference on Artificial Intelligence, 2019 | 124 | 2019 |
| Robot: Robustness-oriented testing for deep learning systems J Wang, J Chen, Y Sun, X Ma, D Wang, J Sun, P Cheng 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE …, 2021 | 97 | 2021 |
| NNrepair: Constraint-based Repair of Neural Network Classifiers M Usman, D Gopinath, Y Sun, Y Noller, C Pasareanu CAV 2021, 2021 | 94 | 2021 |
| DeepConcolic: Testing and debugging deep neural networks Y Sun, X Huang, D Kroening, J Sharp, M Hill, R Ashmore 2019 IEEE/ACM 41st International Conference on Software Engineering …, 2019 | 93 | 2019 |
| HyDiff: Hybrid differential software analysis Y Noller, CS Păsăreanu, M Böhme, Y Sun, HL Nguyen, L Grunske Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020 | 73 | 2020 |
| Coverage-guided testing for recurrent neural networks W Huang, Y Sun, X Zhao, J Sharp, W Ruan, J Meng, X Huang IEEE Transactions on Reliability 71 (3), 1191-1206, 2021 | 70 | 2021 |
| Weakly hard schedulability analysis for fixed priority scheduling of periodic real-time tasks Y Sun, MD Natale ACM Transactions on Embedded Computing Systems (TECS) 16 (5s), 1-19, 2017 | 70 | 2017 |
| trustworthiness of deep neural networks: A survey X Huang, D Kroening, M Kwiatkowska, W Ruan, Y Sun, E Thamo, M Wu, ... arXiv preprint arXiv:1812.08342, 2018 | 59 | 2018 |
| Explaining Image Classifiers using Statistical Fault Localization Y Sun, H Chockler, X Huang, D Kroening ECCV, 2020 | 54 | 2020 |
| Building better bit-blasting for floating-point problems M Brain, F Schanda, Y Sun International Conference on Tools and Algorithms for the Construction and …, 2019 | 50 | 2019 |
| Improving the response time analysis of global fixed-priority multiprocessor scheduling Y Sun, G Lipari, N Guan, W Yi Embedded and Real-Time Computing Systems and Applications (RTCSA), 2014 IEEE …, 2014 | 41 | 2014 |
| Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Norm W Ruan, M Wu, Y Sun, X Huang, D Kroening, M Kwiatkowska arXiv preprint arXiv:1804.05805, 2018 | 40 | 2018 |
| On the ineffectiveness of 1/m-based interference bounds in the analysis of global EDF and FIFO scheduling A Biondi, Y Sun Real-Time Systems 54 (3), 515-536, 2018 | 36 | 2018 |
| A survey of safety and trustworthiness of deep neural networks: verification, testing, adversarial attack and defence, and interpretability. Comput. Sci. Rev. 37, 100270 (2020) X Huang, D Kroening, W Ruan, J Sharp, Y Sun, E Thamo, M Wu, X Yi | 33 | 2020 |
