| Testing deep neural networks Y Sun, X Huang, D Kroening, J Sharp, M Hill, R Ashmore arXiv preprint arXiv:1803.04792, 2018 | 226 | 2018 |
| 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 | 210 | 2018 |
| 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 | 135 | 2020 |
| 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 | 59 | 2019 |
| 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 | 44 | 2019 |
| 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 | 40 | 2018 |
| 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 | 37 | 2017 |
| 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 | 34 | 2014 |
| 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 | 33 | 2019 |
| Parametric schedulability analysis of fixed priority real-time distributed systems Y Sun, R Soulat, G Lipari, É André, L Fribourg Formal Techniques for Safety-Critical Systems, 212-228, 2014 | 24 | 2014 |
| 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 | 22 | 2019 |
| A survey of safety and trustworthiness of deep neural networks X Huang, D Kroening, W Ruan, J Sharp, Y Sun, E Thamo, M Wu, X Yi arXiv preprint arXiv:1812.08342, 2018 | 22 | 2018 |
| 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 | 22 | 2018 |
| 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, 2021 | 20 | 2021 |
| 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 | 20 | 2018 |
| Response time analysis with limited carry-in for global earliest deadline first scheduling Y Sun, G Lipari 2015 IEEE Real-Time Systems Symposium, 130-140, 2015 | 19 | 2015 |
| A pre-order relation for exact schedulability test of sporadic tasks on multiprocessor Global Fixed-Priority scheduling Y Sun, G Lipari Real-Time Systems 52 (3), 323-355, 2016 | 18 | 2016 |
| Verification of two real-time systems using parametric timed automata Y Sun, G Lipari, É André | 18 | 2015 |
| A weak simulation relation for real-time schedulability analysis of global fixed priority scheduling using linear hybrid automata Y Sun, G Lipari Proceedings of the 22nd International Conference on Real-Time Networks and …, 2014 | 18 | 2014 |
| Assessing the pessimism of current multicore global fixed-priority schedulability analysis Y Sun, M Di Natale Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 575-583, 2018 | 15 | 2018 |
