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Deep neural networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety-critical domains.
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An analysis and survey of the development of mutation testing
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DO-178C, software considerations in airborne systems and equipment certification
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Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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TensorFlow: A system for large-scale machine learning
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A survey on data-flow testing
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Rob Ashmore and Elizabeth Lennon · 2017
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DeepXplore: Automated whitebox testing of deep learning systems
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana · 2017
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Safety verification of deep neural networks
Xiaowei Huang, Marta Kwiatkowska, Sen Wang, and Min Wu · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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DeepTest: Automated testing of deep-neural-network-driven autonomous cars
Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray · 2017
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Junhua Ding, Xiaojun Kang, and Xin-Hua Hu · 2017
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Systematic testing of convolutional neural networks for autonomous driving
Tommaso Dreossi, Shromona Ghosh, Alberto Sangiovanni-Vincentelli, and Sanjit A Seshia · 2017
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Reluplex: An efficient SMT solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
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Chih-Hong Cheng, Georg Nührenberg, and Harald Ruess · 2017
Automated directed fairness testing
Sakshi Udeshi, Pryanshu Arora, and Sudipta Chattopadhyay · 2018
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Yuhao Zhang, Yifan Chen, Shing-Chi Cheung, Yingfei Xiong, and Lu Zhang · 2018
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Wei Yang and Tao Xie · 2018
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Simulation-based adversarial test generation for autonomous vehicles with machine learning components
Cumhur Erkan Tuncali, Georgios Fainekos, Hisahiro Ito, and James Kapinski · 2018
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Reasoning about safety of learning-enabled components in autonomous cyber-physical systems
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Output range analysis for deep feedforward neural networks
Souradeep Dutta, Susmit Jha, Sriram Sankaranarayanan, and Ashish Tiwari · 2018
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Output reachable set estimation and verification for multi-layer neural networks
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Safety and trustworthiness of deep neural networks: A survey
Xiaowei Huang et al · 2018
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Feature-guided black-box safety testing of deep neural networks
Matthew Wicker, Xiaowei Huang, and Marta Kwiatkowska · 2018
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Concolic testing for deep neural networks
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Provable defenses against adversarial examples via the convex outer adversarial polytope
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Global robustness evaluation of deep neural networks with provable guarantees for L0 norm
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A game-based approximate verification of deep neural networks with provable guarantees
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Safety assurance objectives for autonomous systems
SASWG · 2019
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Gray-box adversarial testing for control systems with machine learning components
Shakiba Yaghoubi and Georgios Fainekos · 2019
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Guiding deep learning system testing using surprise adequacy
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DeepConcolic: testing and debugging deep neural networks
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Adversarial sample detection for deep neural network through model mutation testing
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A formalization of robustness for deep neural networks
Tommaso Dreossi, Shromona Ghosh, Alberto Sangiovanni-Vincentelli, and Sanjit A Seshia · 2019
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Radoslav Ivanov, James Weimer, Rajeev Alur, George J. Pappas, and Insup Lee · 2019
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Reachability analysis for neural feedback systems using regressive polynomial rule inference
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Formal verification of neural network controlled autonomous systems
Xiaowu Sun, Haitham Khedr, and Yasser Shoukry · 2019
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