{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T12:59:39Z","timestamp":1761569979946,"version":"build-2065373602"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","funder":[{"name":"National Natural Science Foundation of China","award":["62472209"],"award-info":[{"award-number":["62472209"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20221439"],"award-info":[{"award-number":["BK20221439"]}]},{"name":"Primary Research and Development Plan of Jiangsu Province","award":["BE2023025-2"],"award-info":[{"award-number":["BE2023025-2"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,20]]},"DOI":"10.1145\/3755881.3755885","type":"proceedings-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T11:46:17Z","timestamp":1761565577000},"page":"152-162","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Cluster-Based Multi-Objective Metamorphic Test Case Pair Selection for Deep Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1283-5080","authenticated-orcid":false,"given":"Jingling","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4925-7793","authenticated-orcid":false,"given":"Shuwei","family":"Qiu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9401-1777","authenticated-orcid":false,"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4310-9011","authenticated-orcid":false,"given":"Jiyuan","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1383-5421","authenticated-orcid":false,"given":"Huayao","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5786-0894","authenticated-orcid":false,"given":"Xintao","family":"Niu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9575-1012","authenticated-orcid":false,"given":"Changhai","family":"Nie","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Zohreh Aghababaeyan Manel Abdellatif Mahboubeh Dadkhah and Lionel Briand. 2024. Deepgd: A multi-objective black-box test selection approach for deep neural networks. ACM Transactions on Software Engineering and Methodology 33 6 (2024) 1\u201329.","DOI":"10.1145\/3644388"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3512290.3528697"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/SSIRI.2011.21"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"publisher","unstructured":"Earl\u00a0T. Barr Mark Harman Phil McMinn Muzammil Shahbaz and Shin Yoo. 2015. The Oracle Problem in Software Testing: A Survey. IEEE Transactions on Software Engineering 41 5 (2015) 507\u2013525. 10.1109\/TSE.2014.2372785","DOI":"10.1109\/TSE.2014.2372785"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55589-8_4"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.1994.576879"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30217-9_73"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/QSIC.2013.43"},{"key":"e_1_3_3_2_10_2","unstructured":"Tsong\u00a0Y Chen Shing\u00a0C Cheung and Shiu\u00a0Ming Yiu. 2020. Metamorphic testing: a new approach for generating next test cases. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2002.12543 (2020)."},{"key":"e_1_3_3_2_11_2","first-page":"569","volume-title":"Proceedings of the 4th Ibero-American Symposium on Software Engineering and Knowledge Engineering (JIISIC 2004)","author":"Chen Tsong\u00a0Yueh","year":"2004","unstructured":"Tsong\u00a0Yueh Chen, DH Huang, TH Tse, and Zhi\u00a0Quan Zhou. 2004. Case studies on the selection of useful relations in metamorphic testing. In Proceedings of the 4th Ibero-American Symposium on Software Engineering and Knowledge Engineering (JIISIC 2004). Citeseer, 569\u2013583."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Kalyanmoy Deb Amrit Pratap Sameer Agarwal and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6 2 (2002) 182\u2013197.","DOI":"10.1109\/4235.996017"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/IFEEA51475.2020.00199"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3213846.3213858"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397357"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510232"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/SANER56733.2023.00056"},{"key":"e_1_3_3_2_18_2","unstructured":"The Last Driver\u00a0License Holder. 2022. 2021 Disengagement Report from California. https:\/\/thelastdriverlicenseholder.com\/2022\/02\/09\/2021-disengagement-report-from-california\/. Accessed: 2024-10-22."},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/IAECST57965.2022.10062094"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC.2015.179"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00108"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Jinhan Kim Robert Feldt and Shin Yoo. 2023. Evaluating surprise adequacy for deep learning system testing. ACM Transactions on Software Engineering and Methodology 32 2 (2023) 1\u201329.","DOI":"10.1145\/3546947"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/AST52587.2021.00017"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-36336-8_68"},{"key":"e_1_3_3_2_25_2","unstructured":"Alex Krizhevsky Vinod Nair and Geoffrey Hinton. 2009. Cifar-10 and cifar-100 datasets. URl: https:\/\/www. cs. toronto. edu\/kriz\/cifar. html 6 1 (2009) 1."},{"key":"e_1_3_3_2_26_2","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey\u00a0E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012)."},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Yann LeCun L\u00e9on Bottou Yoshua Bengio and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86 11 (1998) 2278\u20132324.","DOI":"10.1109\/5.726791"},{"key":"e_1_3_3_2_28_2","unstructured":"Yu Li Min Li Qiuxia Lai Yannan Liu and Qiang Xu. 2021. Testrank: Bringing order into unlabeled test instances for deep learning tasks. Advances in Neural Information Processing Systems 34 (2021) 20874\u201320886."},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"crossref","unstructured":"Weibo Liu Zidong Wang Xiaohui Liu Nianyin Zeng Yurong Liu and Fuad\u00a0E Alsaadi. 2017. A survey of deep neural network architectures and their applications. Neurocomputing 234 (2017) 11\u201326.","DOI":"10.1016\/j.neucom.2016.12.038"},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238202"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"crossref","unstructured":"Wei Ma Mike Papadakis Anestis Tsakmalis Maxime Cordy and Yves\u00a0Le Traon. 2021. Test selection for deep learning systems. ACM Transactions on Software Engineering and Methodology (TOSEM) 30 2 (2021) 1\u201322.","DOI":"10.1145\/3417330"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.5555\/3066422"},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132785"},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"crossref","unstructured":"Kun Qiu Zheng Zheng Tsong\u00a0Yueh Chen and Pak-Lok Poon. 2020. Theoretical and empirical analyses of the effectiveness of metamorphic relation composition. IEEE Transactions on software engineering 48 3 (2020) 1001\u20131017.","DOI":"10.1109\/TSE.2020.3009698"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"crossref","unstructured":"Megha\u00a0Rani Raigonda et\u00a0al. 2024. Signature Verification System Using SSIM In Image Processing. Journal of Scientific Research and Technology (2024) 5\u201311.","DOI":"10.61808\/jsrt79"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Vincenzo Riccio Gunel Jahangirova Andrea Stocco Nargiz Humbatova Michael Weiss and Paolo Tonella. 2020. Testing machine learning based systems: a systematic mapping. Empirical Software Engineering 25 (2020) 5193\u20135254.","DOI":"10.1007\/s10664-020-09881-0"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Madona\u00a0B Sahaai et\u00a0al. 2021. Brain tumor detection using DNN algorithm. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12 11 (2021) 3338\u20133345.","DOI":"10.17762\/turcomat.v12i11.5946"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"crossref","unstructured":"Umme Sara Morium Akter and Mohammad\u00a0Shorif Uddin. 2019. Image quality assessment through FSIM SSIM MSE and PSNR\u2014a comparative study. Journal of Computer and Communications 7 3 (2019) 8\u201318.","DOI":"10.4236\/jcc.2019.73002"},{"key":"e_1_3_3_2_39_2","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1409.1556 (2014)."},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"crossref","unstructured":"Helge Spieker and Arnaud Gotlieb. 2020. Adaptive metamorphic testing with contextual bandits. Journal of Systems and Software 165 (2020) 110574.","DOI":"10.1016\/j.jss.2020.110574"},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISSREW.2018.000-5"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"crossref","unstructured":"Madhusudan Srinivasan and Upulee Kanewala. 2022. Metamorphic relation prioritization for effective regression testing. Software Testing Verification and Reliability 32 3 (2022) e1807.","DOI":"10.1002\/stvr.1807"},{"key":"e_1_3_3_2_43_2","unstructured":"Madhusudan Srinivasan and Upulee Kanewala. 2022. Prioritization of Metamorphic Relations to reduce the cost of testing. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2209.00162 (2022)."},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"crossref","unstructured":"Madhusudan Srinivasan and Upulee Kanewala. 2024. Improving Early Fault Detection in Machine Learning Systems Using Data Diversity-Driven Metamorphic Relation Prioritization. Electronics 13 17 (2024) 3380.","DOI":"10.3390\/electronics13173380"},{"key":"e_1_3_3_2_45_2","unstructured":"Madhusudan Srinivasan and Upulee Kanewala. 2024. Optimizing Metamorphic Testing: Prioritizing Relations Through Execution Profile Dissimilarity. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2411.09171 (2024)."},{"key":"e_1_3_3_2_46_2","unstructured":"Youcheng Sun Xiaowei Huang Daniel Kroening James Sharp Matthew Hill and Rob Ashmore. 2018. Testing deep neural networks. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1803.04792 (2018)."},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510206"},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"crossref","unstructured":"Tomasz Szanda\u0142a. 2021. Review and comparison of commonly used activation functions for deep neural networks. Bio-inspired neurocomputing (2021) 203\u2013224.","DOI":"10.1007\/978-981-15-5495-7_11"},{"key":"e_1_3_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3324884.3416584"},{"key":"e_1_3_3_2_51_2","doi-asserted-by":"crossref","unstructured":"Zhou Wang Alan\u00a0C Bovik Hamid\u00a0R Sheikh and Eero\u00a0P Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13 4 (2004) 600\u2013612.","DOI":"10.1109\/TIP.2003.819861"},{"key":"e_1_3_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3533767.3534375"},{"key":"e_1_3_3_2_53_2","doi-asserted-by":"crossref","unstructured":"Elaine\u00a0J Weyuker. 1982. On testing non-testable programs. Comput. J. 25 4 (1982) 465\u2013470.","DOI":"10.1093\/comjnl\/25.4.465"},{"key":"e_1_3_3_2_54_2","unstructured":"Han Xiao Kashif Rasul and Roland Vollgraf. 2017. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1708.07747 (2017)."},{"key":"e_1_3_3_2_55_2","unstructured":"Xiaodong Xie Zhehao Li Jinfu Chen Yue Zhang Xiangxiang Wang and Patrick Kwaku\u00a0Kudjo. 2024. MUT Model: A metric for characterizing metamorphic relations diversity. Software Quality Journal (2024) 1\u201343."},{"key":"e_1_3_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3556919"},{"key":"e_1_3_3_2_57_2","doi-asserted-by":"crossref","unstructured":"Zhihao Ying Dave Towey Anthony Graham\u00a0Bellotti and Zhi Quan\u00a0Zhou. 2025. MRGS-ART: Metamorphic Relation and Group Selection Based on Adaptive Random Testing. Software Testing Verification and Reliability 35 1 (2025) e1908.","DOI":"10.1002\/stvr.1908"},{"key":"e_1_3_3_2_58_2","doi-asserted-by":"crossref","unstructured":"Yuan Yuan Chunfu Shao Zhichao Cao Zhaocheng He Changsheng Zhu Yimin Wang and Vlon Jang. 2020. Bus dynamic travel time prediction: using a deep feature extraction framework based on RNN and DNN. Electronics 9 11 (2020) 1876.","DOI":"10.3390\/electronics9111876"},{"key":"e_1_3_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRMS55680.2022.9944550"},{"key":"e_1_3_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/QRS.2019.00056"},{"key":"e_1_3_3_2_61_2","doi-asserted-by":"crossref","unstructured":"Zhi\u00a0Quan Zhou Liqun Sun Tsong\u00a0Yueh Chen and Dave Towey. 2018. Metamorphic relations for enhancing system understanding and use. IEEE Transactions on Software Engineering 46 10 (2018) 1120\u20131154.","DOI":"10.1109\/TSE.2018.2876433"}],"event":{"name":"Internetware 2025: the 16th International Conference on Internetware","sponsor":["SIGSOFT ACM Special Interest Group on Artificial Intelligence"],"location":"Trondheim Norway","acronym":"Internetware 2025"},"container-title":["Proceedings of the 16th International Conference on Internetware"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3755881.3755885","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T11:46:50Z","timestamp":1761565610000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3755881.3755885"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,20]]},"references-count":60,"alternative-id":["10.1145\/3755881.3755885","10.1145\/3755881"],"URL":"https:\/\/doi.org\/10.1145\/3755881.3755885","relation":{},"subject":[],"published":{"date-parts":[[2025,6,20]]},"assertion":[{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}