{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T23:56:02Z","timestamp":1768866962924,"version":"3.49.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Open Fund of the State Key Laboratory for Novel Software Technolog","award":["KFKT2024B28"],"award-info":[{"award-number":["KFKT2024B28"]}]},{"DOI":"10.13039\/501100012456","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["23FZXB053"],"award-info":[{"award-number":["23FZXB053"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Autom Softw Eng"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s10515-025-00581-x","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T13:02:06Z","timestamp":1765198926000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing mutation testing for deep neural networks: a novel approach to generating high-quality mutants"],"prefix":"10.1007","volume":"33","author":[{"given":"Yu","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongming","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenting","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"581_CR1","doi-asserted-by":"crossref","unstructured":"Fanni, S.C., Febi, M., Aghakhanyan, G., Neri, E.: Natural language processing. In: Introduction to artificial intelligence, pp. 87\u201399. Springer (2023)","DOI":"10.1007\/978-3-031-25928-9_5"},{"issue":"22","key":"581_CR2","doi-asserted-by":"publisher","first-page":"4712","DOI":"10.3390\/rs13224712","volume":"13","author":"L Chen","year":"2021","unstructured":"Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., Miao, Y.: Review of image classification algorithms based on convolutional neural networks. Remote Sens. 13(22), 4712 (2021)","journal-title":"Remote Sens."},{"issue":"10","key":"581_CR3","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TASLP.2014.2339736","volume":"22","author":"O Abdel-Hamid","year":"2014","unstructured":"Abdel-Hamid, O., Mohamed, A.-R., Jiang, H., Deng, L., Penn, G., Yu, D.: Convolutional neural networks for speech recognition. IEEE\/ACM Trans. Audio, Speech Language Process 22(10), 1533\u20131545 (2014)","journal-title":"IEEE\/ACM Trans. Audio, Speech Language Process"},{"key":"581_CR4","unstructured":"Zhang, F., Leitner, J., Milford, M., Upcroft, B., Corke, P.: Towards vision-based deep reinforcement learning for robotic motion control. arXiv:1511.03791 (2015)"},{"issue":"1","key":"581_CR5","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1080\/01441647.2018.1494640","volume":"39","author":"A Taeihagh","year":"2019","unstructured":"Taeihagh, A., Lim, H.S.M.: Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transp. Rev. 39(1), 103\u2013128 (2019)","journal-title":"Transp. Rev."},{"issue":"2","key":"581_CR6","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1093\/jamia\/ocw112","volume":"24","author":"E Choi","year":"2017","unstructured":"Choi, E., Schuetz, A., Stewart, W.F., Sun, J.: Using recurrent neural network models for early detection of heart failure onset. J. Am. Med. Inform. Assoc. 24(2), 361\u2013370 (2017)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"9","key":"581_CR7","first-page":"369","volume":"3","author":"S Ingle","year":"2016","unstructured":"Ingle, S., Phute, M.: Tesla autopilot: semi autonomous driving, an uptick for future autonomy. Int. Res. J. Eng. Technol. 3(9), 369\u2013372 (2016)","journal-title":"Int. Res. J. Eng. Technol."},{"issue":"5","key":"581_CR8","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1109\/TSE.2010.62","volume":"37","author":"Y Jia","year":"2010","unstructured":"Jia, Y., Harman, M.: An analysis and survey of the development of mutation testing. IEEE Trans. Software Eng. 37(5), 649\u2013678 (2010)","journal-title":"IEEE Trans. Software Eng."},{"key":"581_CR9","doi-asserted-by":"crossref","unstructured":"Ma, L., Zhang, F., Sun, J., Xue, M., Li, B., Juefei-Xu, F., Xie, C., Li, L., Liu, Y., Zhao, J., et al.: Deepmutation: mutation testing of deep learning systems. In: 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE), pp. 100\u2013111. IEEE (2018)","DOI":"10.1109\/ISSRE.2018.00021"},{"key":"581_CR10","doi-asserted-by":"crossref","unstructured":"Hu, Q., Ma, L., Xie, X., Yu, B., Liu, Y., Zhao, J.: Deepmutation++: a mutation testing framework for deep learning systems. In: 2019 34th IEEE\/ACM international conference on Automated Software Engineering (ASE), pp. 1158\u20131161. IEEE (2019)","DOI":"10.1109\/ASE.2019.00126"},{"key":"581_CR11","doi-asserted-by":"crossref","unstructured":"Humbatova, N., Jahangirova, G., Tonella, P.: Deepcrime: mutation testing of deep learning systems based on real faults. In: Proceedings of the 30th ACM SIGSOFT international symposium on software testing and analysis, pp. 67\u201378 (2021)","DOI":"10.1145\/3460319.3464825"},{"key":"581_CR12","doi-asserted-by":"publisher","first-page":"100164","DOI":"10.1016\/j.crbiot.2023.100164","volume":"7","author":"C Chakraborty","year":"2024","unstructured":"Chakraborty, C., Bhattacharya, M., Pal, S., Lee, S.-S.: From machine learning to deep learning: advances of the recent data-driven paradigm shift in medicine and healthcare. Current Res. Biotech. 7, 100164 (2024)","journal-title":"Current Res. Biotech."},{"issue":"3","key":"581_CR13","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/JPROC.2021.3060483","volume":"109","author":"W Samek","year":"2021","unstructured":"Samek, W., Montavon, G., Lapuschkin, S., Anders, C.J., M\u00fcller, K.-R.: Explaining deep neural networks and beyond: a review of methods and applications. Proc. IEEE 109(3), 247\u2013278 (2021)","journal-title":"Proc. IEEE"},{"issue":"1","key":"581_CR14","first-page":"1","volume":"1","author":"EK Zadeh","year":"2023","unstructured":"Zadeh, E.K., Alaeifard, M.: Adaptive virtual assistant interaction through real-time speech emotion analysis using hybrid deep learning models and contextual awareness. Int. J. Advan. Human Computer Int. 1(1), 1\u201315 (2023)","journal-title":"Int. J. Advan. Human Computer Int."},{"issue":"2","key":"581_CR15","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TITS.2019.2962338","volume":"22","author":"S Kuutti","year":"2020","unstructured":"Kuutti, S., Bowden, R., Jin, Y., Barber, P., Fallah, S.: A survey of deep learning applications to autonomous vehicle control. IEEE Trans. Intell. Transp. Syst. 22(2), 712\u2013733 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"6","key":"581_CR16","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1093\/bib\/bbx044","volume":"19","author":"R Miotto","year":"2018","unstructured":"Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinform. 19(6), 1236\u20131246 (2018)","journal-title":"Brief. Bioinform."},{"key":"581_CR17","doi-asserted-by":"publisher","first-page":"106954","DOI":"10.1016\/j.infsof.2022.106954","volume":"150","author":"Y Li","year":"2022","unstructured":"Li, Y., Shen, W., Wu, T., Chen, L., Wu, D., Zhou, Y., Xu, B.: How higher order mutant testing performs for deep learning models: a fine-grained evaluation of test effectiveness and efficiency improved from second-order mutant-classification tuples. Inf. Softw. Technol. 150, 106954 (2022)","journal-title":"Inf. Softw. Technol."},{"key":"581_CR18","doi-asserted-by":"publisher","first-page":"106413","DOI":"10.1016\/j.infsof.2020.106413","volume":"130","author":"W Shen","year":"2021","unstructured":"Shen, W., Li, Y., Han, Y., Chen, L., Wu, D., Zhou, Y., Xu, B.: Boundary sampling to boost mutation testing for deep learning models. Inf. Softw. Technol. 130, 106413 (2021)","journal-title":"Inf. Softw. Technol."},{"key":"581_CR19","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, Z., Yao, Y., Huang, Z.: A fine-grained evaluation of mutation operators for deep learning systems: a selective mutation approach. In: Proceedings of the 14th asia-pacific symposium on internetware, pp. 123\u2013133 (2023)","DOI":"10.1145\/3609437.3609453"},{"key":"581_CR20","doi-asserted-by":"crossref","unstructured":"Xue, Y., Zhang, Z., Liu, C., Chen, S., Huang, Z.: Deepweak: weak mutation testing for deep learning systems. In: 2024 IEEE 24th international conference on software Quality, Reliability and Security (QRS), pp. 49\u201360. IEEE (2024)","DOI":"10.1109\/QRS62785.2024.00015"},{"key":"581_CR21","doi-asserted-by":"crossref","unstructured":"Eniser, H.F., Gerasimou, S., Sen, A.: Deepfault: fault localization for deep neural networks. In: International conference on fundamental approaches to software engineering, pp. 171\u2013191. Springer (2019)","DOI":"10.1007\/978-3-030-16722-6_10"},{"key":"581_CR22","doi-asserted-by":"crossref","unstructured":"Guo, H., Tao, C., Huang, Z.: Multi-objective white-box test input selection for deep neural network model enhancement. In: 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE), pp. 521\u2013532. IEEE (2023)","DOI":"10.1109\/ISSRE59848.2023.00051"},{"issue":"6","key":"581_CR23","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng, L.: The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Process. Mag. 29(6), 141\u2013142 (2012)","journal-title":"IEEE Signal Process. Mag."},{"key":"581_CR24","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv:1708.07747 (2017)"},{"key":"581_CR25","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"581_CR26","unstructured":"Howard, J.: Imagenette: a smaller subset of 10 easily classified classes from imagenet. https:\/\/github.com\/fastai\/imagenette"},{"key":"581_CR27","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011)"},{"issue":"11","key":"581_CR28","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"581_CR29","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE symposium on Security and Privacy (SP), pp. 39\u201357. IEEE (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"581_CR30","doi-asserted-by":"crossref","unstructured":"Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., Song, D.: Generating adversarial examples with adversarial networks. arXiv:1801.02610 (2018)","DOI":"10.24963\/ijcai.2018\/543"},{"key":"581_CR31","unstructured":"Simonyan, K.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)"},{"key":"581_CR32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"581_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10664-025-10613-5","volume":"30","author":"Z Zhang","year":"2025","unstructured":"Zhang, Z., Wang, Y., Yao, Y., Wang, Z., Huang, Z.: A fine-grained evaluation of mutation operators to boost mutation testing for deep learning systems. Empir. Softw. Eng. 30(3), 1\u201334 (2025)","journal-title":"Empir. Softw. Eng."},{"key":"581_CR34","unstructured":"Kim, J., Humbatova, N., Jahangirova, G., Yoo, S., Tonella, P.: Muff: stable and sensitive post-training mutation testing for deep learning. arXiv:2501.09846 (2025)"},{"key":"581_CR35","doi-asserted-by":"crossref","unstructured":"Palomo-Lozano, F., Estero-Botaro, A., Medina-Bulo, I., N\u00fa\u00f1ez, M.: Test suite minimization for mutation testing of ws-bpel compositions. In: Proceedings of the genetic and evolutionary computation conference, pp. 1427\u20131434 (2018)","DOI":"10.1145\/3205455.3205533"},{"key":"581_CR36","doi-asserted-by":"crossref","unstructured":"Humbatova, N., Jahangirova, G., Tonella, P.: Deepcrime: from real faults to mutation testing tool for deep learning. In: 2023 IEEE\/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), pp. 68\u201372. IEEE (2023)","DOI":"10.1109\/ICSE-Companion58688.2023.00027"},{"key":"581_CR37","doi-asserted-by":"crossref","unstructured":"Riccio, V., Humbatova, N., Jahangirova, G., Tonella, P.: Deepmetis: Augmenting a deep learning test set to increase its mutation score. In: 2021 36th IEEE\/ACM international conference on Automated Software Engineering (ASE), pp. 355\u2013367. IEEE (2021)","DOI":"10.1109\/ASE51524.2021.9678764"},{"issue":"1","key":"581_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3607191","volume":"33","author":"X Dang","year":"2023","unstructured":"Dang, X., Li, Y., Papadakis, M., Klein, J., Bissyand\u00e9, T.F., Le Traon, Y.: Graphprior: mutation-based test input prioritization for graph neural networks. ACM Trans. Softw. Eng. Method. 33(1), 1\u201340 (2023)","journal-title":"ACM Trans. Softw. Eng. Method."},{"key":"581_CR39","doi-asserted-by":"publisher","first-page":"107129","DOI":"10.1016\/j.infsof.2022.107129","volume":"155","author":"F Tambon","year":"2023","unstructured":"Tambon, F., Khomh, F., Antoniol, G.: A probabilistic framework for mutation testing in deep neural networks. Inf. Softw. Technol. 155, 107129 (2023)","journal-title":"Inf. Softw. Technol."},{"key":"581_CR40","doi-asserted-by":"publisher","first-page":"103050","DOI":"10.1016\/j.sysarc.2023.103050","volume":"146","author":"Y Lu","year":"2024","unstructured":"Lu, Y., Shao, K., Zhao, J., Sun, W., Sun, M.: Mutation testing of unsupervised learning systems. J. Syst. Architect. 146, 103050 (2024)","journal-title":"J. Syst. Architect."},{"key":"581_CR41","doi-asserted-by":"crossref","unstructured":"\u00c7etiner, G., Yayan, U., Yaz\u0131c\u0131, A.: Mutation based white box testing of deep neural networks. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3482114"},{"issue":"1","key":"581_CR42","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/TSE.2022.3140510","volume":"49","author":"A Garg","year":"2022","unstructured":"Garg, A., Ojdanic, M., Degiovanni, R., Chekam, T.T., Papadakis, M., Le Traon, Y.: Cerebro: static subsuming mutant selection. IEEE Trans. Software Eng. 49(1), 24\u201343 (2022)","journal-title":"IEEE Trans. Software Eng."},{"issue":"1","key":"581_CR43","doi-asserted-by":"publisher","first-page":"3","DOI":"10.5753\/jserd.2024.3588","volume":"12","author":"S Amorim","year":"2024","unstructured":"Amorim, S., Fernandes, L., Ribeiro, M., Gheyi, R., Delamaro, M., Guimar\u00e3es, M., Santos, A.: Reducing manual efforts in equivalence analysis in mutation testing. J. Softw. Eng. Res. Develop. 12(1), 3\u20131 (2024)","journal-title":"J. Softw. Eng. Res. Develop."},{"issue":"1","key":"581_CR44","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1002\/stvr.1865","volume":"34","author":"S Vercammen","year":"2024","unstructured":"Vercammen, S., Demeyer, S., Borg, M., Pettersson, N., Hedin, G.: Mutation testing optimisations using the clang front-end. Softw. Test Verification Reliab. 34(1), 1865 (2024)","journal-title":"Softw. Test Verification Reliab."},{"key":"581_CR45","doi-asserted-by":"crossref","unstructured":"Arasteh, B., Ghaffari, A.: A cost-effective and machine-learning-based method to identify and cluster redundant mutants in software mutation testing. J Supercomputing, 1\u201333 (2024)","DOI":"10.1007\/s11227-024-06107-8"}],"container-title":["Automated Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-025-00581-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10515-025-00581-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10515-025-00581-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:15:11Z","timestamp":1768821311000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10515-025-00581-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["581"],"URL":"https:\/\/doi.org\/10.1007\/s10515-025-00581-x","relation":{},"ISSN":["0928-8910","1573-7535"],"issn-type":[{"value":"0928-8910","type":"print"},{"value":"1573-7535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,8]]},"assertion":[{"value":"4 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"36"}}