{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T14:07:39Z","timestamp":1780063659113,"version":"3.54.0"},"reference-count":99,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62372071"],"award-info":[{"award-number":["62372071"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Chongqing Technology Innovation and Application Development Project","award":["CSTB2022TIAD-STX0007 and CSTB2023TIAD-STX0025"],"award-info":[{"award-number":["CSTB2022TIAD-STX0007 and CSTB2023TIAD-STX0025"]}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"crossref","award":["CSTB2023NSCQ-MSX0914"],"award-info":[{"award-number":["CSTB2023NSCQ-MSX0914"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2023CDJKYJH013"],"award-info":[{"award-number":["2023CDJKYJH013"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>\n            In recent years, significant progress has been made in testing methods for deep neural networks (DNNs) to ensure their correctness and robustness. Coverage-guided criteria, such as neuron-wise, layer-wise, and path-\/trace-wise, have been proposed for DNN fuzzing. However, existing coverage-based criteria encounter performance bottlenecks for several reasons: \u2776\n            <jats:italic>Testing Adequacy<\/jats:italic>\n            : Partial neural coverage criteria have been observed to achieve full coverage using only a small number of test inputs. In this case, increasing the number of test inputs does not consistently improve the quality of models. \u2777\n            <jats:italic>Interpretability<\/jats:italic>\n            : The current coverage criteria lack interpretability. Consequently, testers are unable to identify and understand which incorrect attributes or patterns of the model are triggered by the test inputs. This lack of interpretability hampers the subsequent debugging and fixing process. Therefore, there is an urgent need for a novel fuzzing criterion that offers improved testing adequacy, better interpretability, and more effective failure detection capabilities for DNNs.\n          <\/jats:p>\n          <jats:p>To alleviate these limitations, we propose NSGen, an approach for DNN fuzzing that utilizes neuron semantics as guidance during test generation. NSGen identifies critical neurons, translates their high-level semantic features into natural language descriptions, and then assembles them into human-readable DNN decision paths (representing the internal decision of the DNN). With these decision paths, we can generate more fault-revealing test inputs by quantifying the similarity between original test inputs and mutated test inputs for fuzzing. We evaluate NSGen on popular DNN models (VGG16_BN, ResNet50, and MobileNet_v2) using CIFAR10, CIFAR100, Oxford 102 Flower, and ImageNet datasets. Compared to 12 existing coverage-guided fuzzing criteria, NSGen outperforms all baselines, increasing the number of triggered faults by 21.4% to 61.2% compared to the state-of-the-art coverage-guided fuzzing criterion. This demonstrates NSGen's effectiveness in generating fault-revealing test inputs through guided input mutation, highlighting its potential to enhance DNN testing and interpretability.<\/jats:p>","DOI":"10.1145\/3688835","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T15:16:35Z","timestamp":1723648595000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Neuron Semantic-Guided Test Generation for Deep Neural Networks Fuzzing"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2603-3366","authenticated-orcid":false,"given":"Li","family":"Huang","sequence":"first","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6013-1369","authenticated-orcid":false,"given":"Weifeng","family":"Sun","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9538-9121","authenticated-orcid":false,"given":"Meng","family":"Yan","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1981-1626","authenticated-orcid":false,"given":"Zhongxin","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4504-6806","authenticated-orcid":false,"given":"Yan","family":"Lei","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4367-7201","authenticated-orcid":false,"given":"David","family":"Lo","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,12,30]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"NSGen. 2024. Retrieved from https:\/\/github.com\/unknownhl\/NSGen"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2014.2339736"},{"key":"e_1_3_2_4_2","article-title":"After Fatal Uber Crash, a Self-Driving Start-Up Moves Forward","author":"Uber Accident","year":"2018","unstructured":"Uber Accident. 2018. After Fatal Uber Crash, a Self-Driving Start-Up Moves Forward. The New York Times.","journal-title":"The New York Times"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00711-8"},{"key":"e_1_3_2_6_2","unstructured":"Mike Aizatsky Kostya Serebryany Oliver Chang Abhishek Arya and Meredith Whittaker. 2016. Announcing OSS-Fuzz: Continuous Fuzzing for Open Source Software. Google Testing Blog."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/2610384.2610413"},{"key":"e_1_3_2_8_2","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473. Retrieved from https:\/\/arxiv.org\/abs\/1409.0473"},{"key":"e_1_3_2_9_2","unstructured":"Nicholas Bai Rahul A. Iyer Tuomas Oikarinen and Tsui-Wei Weng. 2024. Describe-and-dissect: Interpreting neurons in vision networks with language models. arXiv:2403.13771. Retrieved from https:\/\/arxiv.org\/abs\/2403.13771"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2014.2372785"},{"key":"e_1_3_2_11_2","first-page":"48 30071","volume-title":"Proceedings of the National Academy of Sciences","volume":"117","author":"Bau David","year":"2020","unstructured":"David Bau, Jun-Yan Zhu, Hendrik Strobelt, Agata Lapedriza, Bolei Zhou, and Antonio Torralba. 2020. Understanding the role of individual units in a deep neural network. Proceedings of the National Academy of Sciences 117, 48 (2020), 30071\u201330078."},{"key":"e_1_3_2_12_2","unstructured":"Steven Bills Nick Cammarata Dan Mossing Henk Tillman Leo Gao Gabriel Goh Ilya Sutskever Jan Leike Jeff Wu and William Saunders. 2023. Language Models Can Explain Neurons in Language Models. Retrieved May 14 2023 from https:\/\/openaipublic.blob.core.windows.net\/neuron-explainer\/paper\/index.html"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978428"},{"key":"e_1_3_2_14_2","first-page":"3696","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Bordt Sebastian","year":"2023","unstructured":"Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, and Ulrike von Luxburg. 2023. The manifold hypothesis for gradient-based explanations. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 3696\u20133701."},{"key":"e_1_3_2_15_2","first-page":"31","volume-title":"Proceedings of GSCL","volume":"30","author":"Bouma Gerlof","year":"2009","unstructured":"Gerlof Bouma. 2009. Normalized (pointwise) mutual information in collocation extraction. Proceedings of GSCL 30 (2009), 31\u201340."},{"key":"e_1_3_2_16_2","unstructured":"Andrew Brock Jeff Donahue and Karen Simonyan. 2018. Large scale GAN training for high fidelity natural image synthesis. arXiv:1809.11096. Retrieved from https:\/\/arxiv.org\/abs\/1809.11096"},{"key":"e_1_3_2_17_2","unstructured":"Oliver Chang Abhishek Arya Kostya Serebryany and Josh Armour. 2017. OSS-Fuzz: Five Months Later and Rewarding Projects. Google Open Source Blog."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3264593"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098126"},{"key":"e_1_3_2_20_2","first-page":"3642","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.","author":"Ciregan Dan","year":"2012","unstructured":"Dan Ciregan, Ueli Meier, and J\u00fcrgen Schmidhuber. 2012. Multi-column deep neural networks for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 3642\u20133649."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390177"},{"key":"e_1_3_2_22_2","unstructured":"Samet Demir Hasan Ferit Eniser and Alper Sen. 2019. Deepsmartfuzzer: Reward guided test generation for deep learning. arXiv:1911.10621. Retrieved from https:\/\/arxiv.org\/abs\/1911.10621"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_24_2","unstructured":"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"},{"key":"e_1_3_2_25_2","unstructured":"Kedar Dhamdhere Mukund Sundararajan and Qiqi Yan. 2018. How important is a neuron? arXiv:1805.12233. Retrieved from https:\/\/arxiv.org\/abs\/1805.12233"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338954"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460319.3464801"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510232"},{"key":"e_1_3_2_29_2","first-page":"315","volume-title":"Proceedings of the 14th International Conference on Artificial Intelligence and Statistics","author":"Glorot Xavier","year":"2011","unstructured":"Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep sparse rectifier neural networks. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 315\u2013323."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3264835"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409754"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_33_2","volume-title":"International Conference on Learning Representations","author":"Hernandez Evan","year":"2022","unstructured":"Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, and Jacob Andreas. 2022. Natural language descriptions of deep visual features. In International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=NudBMY-tzDr"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00153"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Armand Joulin Edouard Grave Piotr Bojanowski and Tomas Mikolov. 2016. Bag of tricks for efficient text classification. arXiv:1607.01759. Retrieved from https:\/\/arxiv.org\/abs\/1607.01759","DOI":"10.18653\/v1\/E17-2068"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3387940.3391456"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00108"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3546947"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417065"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0933-3657(01)00077-X"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177728730"},{"key":"e_1_3_2_43_2","volume-title":"Learning Multiple Layers of Features from Tiny Images","author":"Krizhevsky Alex","year":"2009","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning Multiple Layers of Features from Tiny Images. University of Toronto, Toronto, Ontario."},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781351251389-8"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397346"},{"key":"e_1_3_2_46_2","doi-asserted-by":"crossref","unstructured":"Lei Ma Felix Juefei-Xu Fuyuan Zhang Jiyuan Sun Minhui Xue Bo Li Chunyang Chen Ting Su Li Li Yang Liu Jianjun Zhao and Yadong Wang. 2018. Deepgauge: Comprehensive and multi-granularity testing criteria for gauging the robustness of deep learning systems. arXiv:1803.07519. Retrieved from https:\/\/arxiv.org\/abs\/1803.07519","DOI":"10.1145\/3238147.3238202"},{"key":"e_1_3_2_47_2","unstructured":"Jiwei Li Michel Galley Chris Brockett Jianfeng Gao and Bill Dolan. 2015. A diversity-promoting objective function for neural conversation models. arXiv:1510.03055. Retrieved from https:\/\/arxiv.org\/abs\/1510.03055"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-NIER.2019.00031"},{"key":"e_1_3_2_49_2","unstructured":"Yinhan Liu Myle Ott Naman Goyal Jingfei Du Mandar Joshi Danqi Chen Omer Levy Mike Lewis Luke Zettlemoyer and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv:1907.11692. Retrieved from https:\/\/arxiv.org\/abs\/1907.11692"},{"key":"e_1_3_2_50_2","first-page":"614","volume-title":"Proceedings of the IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)","author":"Ma Lei","year":"2019","unstructured":"Lei Ma, Felix Juefei-Xu, Minhui Xue, Bo Li, Li Li, Yang Liu, and Jianjun Zhao. 2019. Deepct: Tomographic combinatorial testing for deep learning systems. In Proceedings of the IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 614\u2013618."},{"key":"e_1_3_2_51_2","first-page":"100","volume-title":"Proceedings of the IEEE 29th International Symposium on Software Reliability Engineering (ISSRE)","author":"Ma Lei","year":"2018","unstructured":"Lei Ma, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Felix Juefei-Xu, Chao Xie, Li Li, Yang Liu, Jianjun Zhao, and Yadong Wang. 2018. Deepmutation: Mutation testing of deep learning systems. In Proceedings of the IEEE 29th International Symposium on Software Reliability Engineering (ISSRE). IEEE, 100\u2013111."},{"key":"e_1_3_2_52_2","unstructured":"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"},{"key":"e_1_3_2_53_2","unstructured":"Leland McInnes John Healy and James Melville. 2018. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426. Retrieved from https:\/\/arxiv.org\/abs\/1802.03426"},{"key":"e_1_3_2_54_2","unstructured":"Sharan Narang Colin Raffel Katherine Lee Adam Roberts Noah Fiedel and Karishma Malkan. 2020. Wt5?! training text-to-text models to explain their predictions. arXiv:2004.14546. Retrieved from https:\/\/arxiv.org\/abs\/2004.14546"},{"key":"e_1_3_2_55_2","first-page":"20235","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Neuhaus Yannic","year":"2023","unstructured":"Yannic Neuhaus, Maximilian Augustin, Valentyn Boreiko, and Matthias Hein. 2023. Spurious features everywhere \u2013 large-scale detection of harmful spurious features in ImageNet. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), 20235\u201320246."},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"e_1_3_2_57_2","first-page":"4901","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Odena Augustus","year":"2019","unstructured":"Augustus Odena, Catherine Olsson, David Andersen, and Ian Goodfellow. 2019. Tensorfuzz: Debugging neural networks with coverage-guided fuzzing. In Proceedings of the International Conference on Machine Learning. PMLR, 4901\u20134911."},{"key":"e_1_3_2_58_2","unstructured":"Tuomas Oikarinen and Tsui-Wei Weng. 2022. Clip-dissect: Automatic description of neuron representations in deep vision networks. arXiv:2204.10965. Retrieved from https:\/\/arxiv.org\/abs\/2204.10965"},{"key":"e_1_3_2_59_2","doi-asserted-by":"crossref","unstructured":"Kexin Pei. 2017. Deepxplore Code Release. Retrieved from https:\/\/github.com\/peikexin9\/deepxplore\/","DOI":"10.1145\/3132747.3132785"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132785"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1016\/0022-5193(66)90013-0"},{"key":"e_1_3_2_63_2","first-page":"8748","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning. PMLR, 8748\u20138763."},{"issue":"8","key":"e_1_3_2_64_2","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford Alec","year":"2019","unstructured":"Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog 1, 8 (2019), 9.","journal-title":"OpenAI Blog"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01755"},{"key":"e_1_3_2_66_2","first-page":"1","article-title":"Vuzzer: Application-aware evolutionary fuzzing","volume":"17","author":"Rawat Sanjay","year":"2017","unstructured":"Sanjay Rawat, Vivek Jain, Ashish Kumar, Lucian Cojocar, Cristiano Giuffrida, and Herbert Bos. 2017. Vuzzer: Application-aware evolutionary fuzzing. In Proceedings of the International Conference on Network and Distributed System Security (NDSS), Vol. 17, 1\u201314.","journal-title":"Proceedings of the International Conference on Network and Distributed System Security (NDSS)"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409730"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-NIER.2019.00030"},{"key":"e_1_3_2_71_2","first-page":"157","volume-title":"Proceedings of the IEEE Cybersecurity Development (SecDev)","author":"Serebryany Kosta","year":"2016","unstructured":"Kosta Serebryany. 2016. Continuous fuzzing with libfuzzer and addresssanitizer. In Proceedings of the IEEE Cybersecurity Development (SecDev). IEEE, 157\u2013157."},{"key":"e_1_3_2_72_2","unstructured":"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"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/0304-3991(81)90061-9"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238172"},{"key":"e_1_3_2_76_2","first-page":"3319","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Sundararajan Mukund","year":"2017","unstructured":"Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In Proceedings of the International Conference on Machine Learning. PMLR, 3319\u20133328."},{"key":"e_1_3_2_77_2","unstructured":"Robert Swiecki and F Gr\u00f6bert. 2016. Honggfuzz. Retrieved from http:\/\/code.google.com\/p\/honggfuzz"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180220"},{"key":"e_1_3_2_79_2","first-page":"119","volume-title":"Proceedings of the IEEE International Conference On Artificial Intelligence Testing (AITest)","author":"Wang Dong","year":"2019","unstructured":"Dong Wang, Ziyuan Wang, Chunrong Fang, Yanshan Chen, and Zhenyu Chen. 2019. DeepPath: Path-driven testing criteria for deep neural networks. In Proceedings of the IEEE International Conference On Artificial Intelligence Testing (AITest). IEEE, 119\u2013120."},{"key":"e_1_3_2_80_2","first-page":"408","volume-title":"Proceedings of the 24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS \u201918)","author":"Wicker Matthew","year":"2018","unstructured":"Matthew Wicker, Xiaowei Huang, and Marta Kwiatkowska. 2018. Feature-guided black-box safety testing of deep neural networks. In Proceedings of the 24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS \u201918). Springer, 408\u2013426."},{"key":"e_1_3_2_81_2","unstructured":"Kai Xiao Logan Engstrom Andrew Ilyas and Aleksander Madry. 2020. Noise or signal: The role of image backgrounds in object recognition. arXiv:2006.09994. Retrieved from https:\/\/arxiv.org\/abs\/2006.09994"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1145\/3490489"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/3293882.3330579"},{"key":"e_1_3_2_84_2","first-page":"5772","volume-title":"Proceedings of the International Joint Conferences on Artificial Intelligence Organization","author":"Xie Xiaofei","year":"2019","unstructured":"Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, and Xiaohong Li. 2019. Diffchaser: Detecting disagreements for deep neural networks. In Proceedings of the International Joint Conferences on Artificial Intelligence Organization, 5772\u20135778."},{"key":"e_1_3_2_85_2","first-page":"2048","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Xu Kelvin","year":"2015","unstructured":"Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In Proceedings of the International Conference on Machine Learning. PMLR, 2048\u20132057."},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3244340"},{"key":"e_1_3_2_87_2","first-page":"e2561","article-title":"Revisiting deep neural network test coverage from the test effectiveness perspective","volume":"36","author":"Yan Ming","year":"2023","unstructured":"Ming Yan, Junjie Chen, Xuejie Cao, Zhuo Wu, Yuning Kang, and Zan Wang. 2023. Revisiting deep neural network test coverage from the test effectiveness perspective. Journal of Software: Evolution and Process 36 (2023), e2561.","journal-title":"Journal of Software: Evolution and Process"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409671"},{"key":"e_1_3_2_89_2","first-page":"408","volume-title":"Proceedings of the IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)","author":"Yang Zhou","year":"2022","unstructured":"Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, and David Lo. 2022. Revisiting neuron coverage metrics and quality of deep neural networks. In Proceedings of the IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 408\u2013419."},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3561157"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00107"},{"key":"e_1_3_2_92_2","volume-title":"American Fuzzy Lop","author":"Zalewski Michal","year":"2017","unstructured":"Michal Zalewski. 2017. American Fuzzy Lop."},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00688"},{"key":"e_1_3_2_94_2","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238187"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380331"},{"issue":"1","key":"e_1_3_2_96_2","first-page":"1","article-title":"Seed Selection for Testing Deep Neural Networks","volume":"33","author":"Zhi Yuhan","year":"2023","unstructured":"Yuhan Zhi, Xiaofei Xie, Chao Shen, Jun Sun, Xiaoyu Zhang, and Xiaohong Guan. 2023. Seed Selection for Testing Deep Neural Networks. ACM Transactions on Software Engineering and Methodology 33, 1 (2023), 1\u201333.","journal-title":"ACM Transactions on Software Engineering and Methodology"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723009"},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460319.3464811"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544792"}],"container-title":["ACM Transactions on Software Engineering and Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688835","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3688835","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:10Z","timestamp":1750291450000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688835"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,30]]},"references-count":99,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1,31]]}},"alternative-id":["10.1145\/3688835"],"URL":"https:\/\/doi.org\/10.1145\/3688835","relation":{},"ISSN":["1049-331X","1557-7392"],"issn-type":[{"value":"1049-331X","type":"print"},{"value":"1557-7392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,30]]},"assertion":[{"value":"2023-11-06","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-28","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}