Zohreh Aghababaeyan, Manel Abdellatif, Lionel Briand, S. Ramesh, and Mojtaba Bagherzadeh. 2023. Black-box testing of deep neural networks through test case diversity. IEEE Transactions on Software Engineering 49 (2023), 3182–3204.
Hamzah Al-Qadasi, Yliès Falcone, and Saddek Bensalem. 2023. Difficulty and severity-oriented metrics for test prioritization in deep learning systems. In 2023 IEEE International Conference on Artificial Intelligence Testing (AITest). IEEE, 40–48.
Nadia Alshahwan, Mark Harman, and Alexandru Marginean. 2023. Software testing research challenges: An industrial perspective. In 2023 IEEE Conference on Software Testing, Verification and Validation (ICST). IEEE, 1–10.
Zeynep Batmaz, Ali Yurekli, Alper Bilge, and Cihan Kaleli. 2019. A review on deep learning for recommender systems: Challenges and remedies. Artificial Intelligence Review 52 (2019), 1–37.
Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer. 2017. Adversarial patch. arXiv:1712.09665. Retrieved from https://arxiv.org/abs/1712.09665
Taejoon Byun, Vaibhav Sharma, Abhishek Vijayakumar, Sanjai Rayadurgam, and Darren Cofer. 2019. Input prioritization for testing neural networks. In 2019 IEEE International Conference on Artificial Intelligence Testing (AITest). IEEE, 63–70.
Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 IEEE Symposium on Security and Privacy (SP). IEEE, 39–57.
Venkat Chandrasekaran and Parikshit Shah. 2017. Relative entropy optimization and its applications. Mathematical Programming 161 (2017), 1–32.
Junjie Chen, Guancheng Wang, Dan Hao, Yingfei Xiong, Hongyu Zhang, Lu Zhang, and Bing Xie. 2018. Coverage prediction for accelerating compiler testing. IEEE Transactions on Software Engineering 47, 2 (2018), 261–278.
Junjie Chen, Zhuo Wu, Zan Wang, Hanmo You, Lingming Zhang, and Ming Yan. 2020. Practical accuracy estimation for efficient deep neural network testing. ACM Transactions on Software Engineering and Methodology 29, 4 (2020), 1–35.
Qi Chen, Sihai Tang, Qing Yang, and Song Fu. 2019. Cooper: Cooperative perception for connected autonomous vehicles based on 3D point clouds. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, 514–524.
Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
Tsong Y. Chen, Shing C. Cheung, and Shiu Ming Yiu .2020. Metamorphic testing: A new approach for generating next test cases. arXiv:2002.12543. Retrieved from https://arxiv.org/abs/2002.12543
Wei-Yu Chiu, Gary G. Yen, and Teng-Kuei Juan. 2016. Minimum Manhattan distance approach to multiple criteria decision making in multiobjective optimization problems. IEEE Transactions on Evolutionary Computation 20, 6 (2016), 972–985.
Tejalal Choudhary, Vipul Mishra, Anurag Goswami, and Jagannathan Sarangapani. 2020. A comprehensive survey on model compression and acceleration. Artificial Intelligence Review 53 (2020), 5113–5155.
Israel Cohen, Yiteng Huang, Jingdong Chen, Jacob Benesty, Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen. 2009. Pearson correlation coefficient. Noise Reduction in Speech Processing 2 (2009), 1–4.
Xueqi Dang, Yinghua Li, Mike Papadakis, Jacques Klein, Tegawendé F. Bissyandé, and Yves L. E. Traon. 2023. GraphPrior: Mutation-based test input prioritization for graph neural networks. ACM Transactions on Software Engineering and Methodology 33 (2023), 1–40.
Robert David, Jared Duke, Advait Jain, Vijay Janapa Reddi, Nat Jeffries, Jian Li, Nick Kreeger, Ian Nappier, Meghna Natraj, Tiezhen Wang, et al. 2021. TensorFlow lite micro: Embedded machine learning for TinyML systems. Proceedings of Machine Learning and Systems 3 (2021), 800–811.
Li Deng. 2012. The MNIST database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine 29, 6 (2012), 141–142.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. Retrieved from https://arxiv.org/abs/1810.04805
Rahul Dey and Fathi M. Salem. 2017. Gate-variants of gated recurrent unit (GRU) neural networks. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 1597–1600.
Daniel Di Nardo, Nadia Alshahwan, Lionel Briand, and Yvan Labiche. 2013. Coverage-based test case prioritisation: An industrial case study. In 2013 IEEE 6th International Conference on Software Testing, Verification and Validation. IEEE, 302–311.
Len Du. 2020. How much deep learning does neural style transfer really need? An ablation study. In IEEE/CVF Winter Conference on Applications of Computer Vision, 3150–3159.
Sebastian Elbaum, Alexey G. Malishevsky, and Gregg Rothermel. 2002. Test case prioritization: A family of empirical studies. IEEE Transactions on Software Engineering 28, 2 (2002), 159–182.
Sebastian Elbaum, Gregg Rothermel, and John Penix. 2014. Techniques for improving regression testing in continuous integration development environments. In 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, 235–245.
Emelie Engström, Per Runeson, and Mats Skoglund. 2010. A systematic review on regression test selection techniques. Information and Software Technology 52, 1 (2010), 14–30.
Umi Fadlilah, Abd Kadir Mahamad, and Bana Handaga. 2021. The development of android for Indonesian sign language using TensorFlow lite and CNN: An initial study. In Journal of Physics: Conference Series, Vol. 1858. IOP Publishing, 012085.
Yang Feng, Qingkai Shi, Xinyu Gao, Jun Wan, Chunrong Fang, and Zhenyu Chen. 2020. DeepGini: Prioritizing massive tests to enhance the robustness of deep neural networks. In 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, 177–188.
Xinyu Gao, Yang Feng, Yining Yin, Zixi Liu, Zhenyu Chen, and Baowen Xu. 2022. Adaptive test selection for deep neural networks. In 44th International Conference on Software Engineering, 73–85.
Víctor González-Castro, Rocío Alaiz-Rodríguez, and Enrique Alegre. 2013. Class distribution estimation based on the Hellinger distance. Information Sciences 218 (2013), 146–164.
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv:1412.6572. Retrieved from https://arxiv.org/abs/1412.6572
Yao Hao, Zhiqiu Huang, Hongjing Guo, and Guohua Shen. 2023. Test input selection for deep neural network enhancement based on multiple-objective optimization. In 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 534–545.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
Hadi Hemmati, Andrea Arcuri, and Lionel Briand. 2013. Achieving scalable model-based testing through test case diversity. ACM Transactions on Software Engineering and Methodology 22, 1 (2013), 1–42.
Christopher Henard, Mike Papadakis, Mark Harman, Yue Jia, and Yves Le Traon. 2016. Comparing white-box and black-box test prioritization. In 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE). IEEE, 523–534.
Christopher Henard, Mike Papadakis, Gilles Perrouin, Jacques Klein, Patrick Heymans, and Yves Le Traon. 2014. Bypassing the combinatorial explosion: Using similarity to generate and prioritize t-wise test configurations for software product lines. IEEE Transactions on Software Engineering 40, 7 (2014), 650–670.
Han Hu, Yujin Huang, Qiuyuan Chen, Terry Yue Zhuo, and Chunyang Chen. 2023. A first look at on-device models in iOS apps. ACM Transactions on Software Engineering and Methodology 33, 1 (2023), 1–30.
Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Wei Ma, Mike Papadakis, and Yves Le Traon. 2021. Towards exploring the limitations of active learning: An empirical study. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 917–929.
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. 2017. Densely connected convolutional networks. In IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.
Xinyu Huang, Xinjing Cheng, Qichuan Geng, Binbin Cao, Dingfu Zhou, Peng Wang, Yuanqing Lin, and Ruigang Yang. 2018. The ApolloScape dataset for autonomous driving. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, 954–960.
Nargiz Humbatova, Gunel Jahangirova, and Paolo Tonella. 2021. DeepCrime: Mutation testing of deep learning systems based on real faults. In 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, 67–78.
Gunel Jahangirova and Paolo Tonella. 2020. An empirical evaluation of mutation operators for deep learning systems. In 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST). IEEE, 74–84.
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems, Vol. 30.
Been Kim, Rajiv Khanna, and Oluwasanmi O. Koyejo. 2016. Examples are not enough, learn to criticize! Criticism for interpretability. In Advances in Neural Information Processing Systems, Vol. 29.
Tae KyunKim. 2015. T test as a parametric statistic. Korean Journal of Anesthesiology 68, 6 (2015), 540–546.
Torleiv Klove, Te-Tsung Lin, Shi-Chun Tsai, and Wen-Guey Tzeng. 2010. Permutation arrays under the Chebyshev distance. IEEE Transactions on Information Theory 56, 6 (2010), 2611–2617.
Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. University of Toronto 2009, 32–33.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25.
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. 2018. Adversarial examples in the physical world. In Artificial Intelligence Safety and Security. Roman V. Yampolskiy (Ed.), Chapman and Hall/CRC, 99–112.
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 11 (1998), 2278–2324.
Yves Ledru, Alexandre Petrenko, Sergiy Boroday, and Nadine Mandran. 2012. Prioritizing test cases with string distances. Automated Software Engineering 19, 1 (2012), 65–95.
Yinghua Li, Xueqi Dang, Haoye Tian, Tiezhu Sun, Zhijie Wang, Lei Ma, Jacques Klein, and Tegawende F. Bissyande. 2022. AI-driven mobile apps: An Explorative Study. arXiv:2212.01635. Retrieved from https://arxiv.org/abs/2212.01635
Zheng Li, Mark Harman, and Robert M. Hierons. 2007. Search algorithms for regression test case prioritization. IEEE Transactions on Software Engineering 33, 4 (2007), 225–237.
Zenan Li, Xiaoxing Ma, Chang Xu, Chun Cao, Jingwei Xu, and Jian Lü. 2019. Boosting operational DNN testing efficiency through conditioning. In 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 499–509.
Leo Liberti, Carlile Lavor, Nelson Maculan, and Antonio Mucherino. 2014. Euclidean distance geometry and applications. SIAM Review 56, 1 (2014), 3–69.
Min Lin, Qiang Chen, and Shuicheng Yan. 2013. Network in network. arXiv:1312.4400. Retrieved from https://arxiv.org/abs/1312.4400
Zixi Liu, Yang Feng, Yining Yin, and Zhenyu Chen. 2022. DeepState: Selecting test suites to enhance the robustness of recurrent neural networks. In 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE), 598–609.
Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, and Qiang Yang. 2019. Autocross: Automatic feature crossing for tabular data in real-world applications. In 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1936–1945.
Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, et al. 2018. Deepgauge: Multi-granularity testing criteria for deep learning systems. In 33rd ACM/IEEE International Conference on Automated Software Engineering, 120–131.
Lei Ma, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Felix Juefei-Xu, Chao Xie, Li Li, Yang Liu, Jianjun Zhao, et al. 2018. Deepmutation: Mutation testing of deep learning systems. In 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE). IEEE, 100–111.
Wei Ma, Mike Papadakis, Anestis Tsakmalis, Maxime Cordy, and Yves Le Traon. 2021. Test selection for deep learning systems. ACM Transactions on Software Engineering and Methodology 30, 2 (2021), 1–22.
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks. arXiv:1706.06083. Retrieved from https://arxiv.org/abs/1706.06083
Nattaya Mairittha, Tittaya Mairittha, and Sozo Inoue. 2019. On-device deep learning inference for efficient activity data collection. Sensors 19, 15 (2019), 3434.
Nattaya Mairittha, Tittaya Mairittha, and Sozo Inoue. 2020. Improving activity data collection with on-device personalization using fine-tuning. In 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2020 ACM International Symposium on Wearable Computers, 255–260.
Nattaya Mairittha, Tittaya Mairittha, and Sozo Inoue. 2020. On-device deep personalization for robust activity data collection. Sensors 21, 1 (2020), 41.
M. D. Malkauthekar. 2013. Analysis of Euclidean distance and Manhattan distance measure in face recognition. In 3rd International Conference on Computational Intelligence and Information Technology (CIIT’13). IET, 503–507.
Tong Meng, Xuyang Jing, Zheng Yan, and Witold Pedrycz. 2020. A survey on machine learning for data fusion. Information Fusion 57 (2020), 115–129.
Agnieszka Mikołajczyk and Michał Grochowski. 2018. Data augmentation for improving deep learning in image classification problem. In 2018 International Interdisciplinary PhD Workshop (IIPhDW). IEEE, 117–122.
Sharada P. Mohanty, David P. Hughes, and Marcel Salathé. 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7 (2016), 1419.
Hieu V. Nguyen and Li Bai. 2010. Cosine similarity metric learning for face verification. In Asian Conference on Computer Vision. Springer, 709–720.
Quang Hung Nguyen, Hai-Bang Ly, Lanh Si Ho, Nadhir Al-Ansari, Hiep Van Le, Van Quan Tran, Indra Prakash, and Binh Thai Pham. 2021. Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering 2021 (2021), 1–15.
Augustus Odena, Catherine Olsson, David Andersen, and Ian Goodfellow. 2019. TensorFuzz: Debugging neural networks with coverage-guided fuzzing. In International Conference on Machine Learning. PMLR, 4901–4911.
Rongqi Pan, Mojtaba Bagherzadeh, Taher A. Ghaleb, and Lionel Briand. 2022. Test case selection and prioritization using machine learning: A systematic literature review. Empirical Software Engineering 27, 2 (2022), 29.
Victor M. Panaretos and Yoav Zemel. 2019. Statistical aspects of Wasserstein distances. Annual Review of Statistics and Its Application 6 (2019), 405–431.
Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella. 2017. Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets. IEEE Transactions on Software Engineering 44, 2 (2017), 122–158.
Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella. 2018. A large scale empirical comparison of state-of-the-art search-based test case generators. Information and Software Technology 104 (2018), 236–256.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems, Vol. 32.
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. 2017. DeepXplore: Automated whitebox testing of deep learning systems. In 26th Symposium on Operating Systems Principles, 1–18.
Luis Perez and Jason Wang. 2017. The effectiveness of data augmentation in image classification using deep learning. arXiv:1712.04621. Retrieved from https://arxiv.org/abs/1712.04621
Friedrich Pillichshammer. 2000. On the sum of squared distances in the Euclidean plane. Archiv der Mathematik 74, 6 (2000), 472–480.
Antonio Polino, Razvan Pascanu, and Dan Alistarh. 2018. Model compression via distillation and quantization. arXiv:1802.05668. Retrieved from https://arxiv.org/abs/1802.05668
Gregg Rothermel, Roland H. Untch, Chengyun Chu, and Mary Jean Harrold. 2001. Prioritizing test cases for regression testing. IEEE Transactions on Software Engineering 27, 10 (2001), 929–948.
Neha Sharma, Vibhor Jain, and Anju Mishra. 2018. An analysis of convolutional neural networks for image classification. Procedia Computer Science 132 (2018), 377–384.
Connor Shorten and Taghi M. Khoshgoftaar. 2019. A survey on image data augmentation for deep learning. Journal of Big Data 6, 1 (2019), 1–48.
Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. Retrieved from https://arxiv.org/abs/1409.1556
Zhichuang Sun, Ruimin Sun, Long Lu, and Alan Mislove. 2021. Mind your weight (s): A large-scale study on insufficient machine learning model protection in mobile apps. In 30th USENIX Security Symposium (USENIX Security ’21), 1955–1972.
Yali Tao, Chuanqi Tao, Hongjing Guo, and Bohan Li. 2022. TPFL: Test input prioritization for deep neural networks based on fault localization. In International Conference on Advanced Data Mining and Applications. Springer, 368–383.
Luke Taylor and Geoff Nitschke. 2018. Improving deep learning with generic data augmentation. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 1542–1547.
Mohit Thakkar and Mohit Thakkar. 2019. Introduction to core ML framework. In Beginning Machine Learning in iOS: CoreML Framework, Springer, 15–49.
Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray. 2018. Deeptest: Automated testing of deep-neural-network-driven autonomous cars. In 40th International Conference on Software Engineering, 303–314.
Yongqiang Tian, Wuqi Zhang, Ming Wen, Shing-Chi Cheung, Chengnian Sun, Shiqing Ma, and Yu Jiang. 2023. Finding deviated behaviors of the compressed DNN models for image classifications. ACM Transactions on Software Engineering and Methodology 32, 5 (2023), 1–32.
Paolo Tonella, Paolo Avesani, and Angelo Susi. 2006. Using the case-based ranking methodology for test case prioritization. In 2006 22nd IEEE International Conference on Software Maintenance. IEEE, 123–133.
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Thanh Nguyen, and Sai-Kit Yeung. 2019. Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data. In IEEE/CVF International Conference on Computer Vision, 1588–1597.
Dan Wang and Yi Shang. 2014. A new active labeling method for deep learning. In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 112–119.
Zan Wang, Hanmo You, Junjie Chen, Yingyi Zhang, Xuyuan Dong, and Wenbin Zhang. 2021. Prioritizing test inputs for deep neural networks via mutation analysis. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). IEEE, 397–409.
Zhengyuan Wei, Haipeng Wang, Imran Ashraf, and W. K. Chan. 2022. Predictive mutation analysis of test case prioritization for deep neural networks. In 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS). IEEE, 682–693.
Michael Weiss and Paolo Tonella. 2022. Simple techniques work surprisingly well for neural network test prioritization and active learning (replicability study). In 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, 139–150.
Xiaoxue Wu, Jinjin Shen, Wei Zheng, Lidan Lin, Yulei Sui, and Abubakar Omari Abdallah Semasaba. 2023. RNNtcs: A test case selection method for recurrent neural networks. Knowledge-Based Systems 279 (2023), 110955.
Zhuo Wu, Zan Wang, Junjie Chen, Hanmo You, Ming Yan, and Lanjun Wang. 2024. Stratified random sampling for neural network test input selection. Information and Software Technology 165 (2024), 107331.
Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017. Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms. arXiv:1708.07747. Retrieved from https://arxiv.org/abs/1708.07747
Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, and Xiaohong Li. 2019. Diffchaser: Detecting disagreements for deep neural networks. In International Joint Conferences on Artificial Intelligence Organization.
Mengwei Xu, Jiawei Liu, Yuanqiang Liu, Felix Xiaozhu Lin, Yunxin Liu, and Xuanzhe Liu. 2019. A first look at deep learning apps on smartphones. In the World Wide Web Conference, 2125–2136.
Ahmed Haj Yahmed, Houssem Ben Braiek, Foutse Khomh, Sonia Bouzidi, and Rania Zaatour. 2022. DiverGet: A search-based software testing approach for deep neural network quantization assessment. Empirical Software Engineering 27, 7 (2022), 193.
Shin Yoo and Mark Harman. 2012. Regression testing minimization, selection and prioritization: A survey. Software Testing, Verification and Reliability 22, 2 (2012), 67–120.
Shin Yoo, Mark Harman, Paolo Tonella, and Angelo Susi. 2009. Clustering test cases to achieve effective and scalable prioritisation incorporating expert knowledge. In 18th International Symposium on Software Testing and Analysis, 201–212.
Yong Yu, Xiaosheng Si, Changhua Hu, and Jianxun Zhang. 2019. A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation 31, 7 (2019), 1235–1270.
Xiangyu Yue, Bichen Wu, Sanjit A. Seshia, Kurt Keutzer, and Alberto L. Sangiovanni-Vincentelli. 2018. A lidar point cloud generator: From a virtual world to autonomous driving. In 2018 ACM on International Conference on Multimedia Retrieval, 458–464.
Jie M. Zhang, Mark Harman, Lei Ma, and Yang Liu. 2020. Machine learning testing: Survey, landscapes and horizons. IEEE Transactions on Software Engineering 48, 1 (2020), 1–36.
Haibin Zheng, Jinyin Chen, and Haibo Jin. 2023. CertPri: Certifiable prioritization for deep neural networks via movement cost in feature space. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 1–13.
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen. 2017. Incremental network quantization: Towards lossless CNNs with low-precision weights. arXiv:1702.03044. Retrieved from https://arxiv.org/abs/1702.03044
