{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T20:25:23Z","timestamp":1768681523857,"version":"3.49.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031212123","type":"print"},{"value":"9783031212130","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21213-0_2","type":"book-chapter","created":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T06:08:19Z","timestamp":1670652499000},"page":"22-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MTUL: Towards Mutation Testing of\u00a0Unsupervised Learning Systems"],"prefix":"10.1007","author":[{"given":"Yuteng","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaicheng","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,11]]},"reference":[{"issue":"9","key":"2_CR1","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.1080\/14697688.2019.1622311","volume":"19","author":"ML de Prado","year":"2019","unstructured":"de Prado, M.L., Lewis, M.J.: Detection of false investment strategies using unsupervised learning methods. Quant. Finance 19(9), 1555\u20131565 (2019)","journal-title":"Quant. Finance"},{"issue":"1","key":"2_CR2","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/0165-1684(94)90182-1","volume":"36","author":"HM Abbas","year":"1994","unstructured":"Abbas, H.M., Fahmy, M.M.: Neural networks for maximum likelihood clustering. Signal Process. 36(1), 111\u2013126 (1994)","journal-title":"Signal Process."},{"issue":"1","key":"2_CR3","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/TR.2015.2461676","volume":"65","author":"W Liu","year":"2016","unstructured":"Liu, W., Liu, S., Qing, G., Chen, J., Chen, X., Chen, D.: Empirical studies of a two-stage data preprocessing approach for software fault prediction. IEEE Trans. Reliab. 65(1), 38\u201353 (2016)","journal-title":"IEEE Trans. Reliab."},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Kos, J., Fischer, I., Song, D.: Adversarial examples for generative models. In: 2018 IEEE Security and Privacy Workshops, SP Workshops 2018, San Francisco, CA, USA, 24 May 2018, pp. 36\u201342. IEEE Computer Society (2018)","DOI":"10.1109\/SPW.2018.00014"},{"issue":"11","key":"2_CR5","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1145\/3361566","volume":"62","author":"K Pei","year":"2019","unstructured":"Pei, K., Cao, Y., Yang, J., Jana, S.: Deepxplore: automated whitebox testing of deep learning systems. Commun. ACM 62(11), 137\u2013145 (2019)","journal-title":"Commun. ACM"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Ma, L., et al.: Deepgauge: multi-granularity testing criteria for deep learning systems. In: Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering, ASE 2018, Montpellier, France, 3\u20137 September 2018, pp. 120\u2013131. ACM (2018)","DOI":"10.1145\/3238147.3238202"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M., Ashmore, R.: Structural test coverage criteria for deep neural networks. ACM Trans. Embed. Comput. Syst. 18(5s), 94:1\u201394:23 (2019)","DOI":"10.1145\/3358233"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Kim, J., Feldt, R., Yoo, S.: Guiding deep learning system testing using surprise adequacy. In: Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, Montreal, QC, Canada, 25\u201331 May 2019, pp. 1039\u20131049. IEEE\/ACM (2019)","DOI":"10.1109\/ICSE.2019.00108"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Gerasimou, S., Eniser, H.F., Sen, A., Cakan, A.: Importance-driven deep learning system testing. In: ICSE 2020: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June\u201319 July 2020, pp. 702\u2013713. ACM (2020)","DOI":"10.1145\/3377811.3380391"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Ma, L., et al.: Deepmutation: mutation testing of deep learning systems. In: 29th IEEE International Symposium on Software Reliability Engineering, ISSRE 2018, Memphis, TN, USA, 15\u201318 October 2018, pp. 100\u2013111. IEEE Computer Society (2018)","DOI":"10.1109\/ISSRE.2018.00021"},{"key":"2_CR11","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: 34th IEEE\/ACM International Conference on Automated Software Engineering, ASE 2019, San Diego, CA, USA, 11\u201315 November 2019, pp. 1158\u20131161. IEEE (2019)","DOI":"10.1109\/ASE.2019.00126"},{"key":"2_CR12","unstructured":"Uesato, J., et al.: Rigorous agent evaluation: an adversarial approach to uncover catastrophic failures. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019)"},{"key":"2_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-3-030-91265-9_8","volume-title":"Dependable Software Engineering. Theories, Tools, and Applications","author":"Y Lu","year":"2021","unstructured":"Lu, Y., Sun, W., Sun, M.: Mutation testing of reinforcement learning systems. In: Qin, S., Woodcock, J., Zhang, W. (eds.) SETTA 2021. LNCS, vol. 13071, pp. 143\u2013160. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-91265-9_8"},{"issue":"4","key":"2_CR14","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1109\/TR.2020.2972266","volume":"69","author":"X Xie","year":"2020","unstructured":"Xie, X., Zhang, Z., Chen, T.Y., Liu, Y., Poon, P.-L., Xu, B.: METTLE: a metamorphic testing approach to assessing and validating unsupervised machine learning systems. IEEE Trans. Reliab. 69(4), 1293\u20131322 (2020)","journal-title":"IEEE Trans. Reliab."},{"issue":"5","key":"2_CR15","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1109\/TSE.2010.62","volume":"37","author":"Y Jia","year":"2011","unstructured":"Jia, Y., Harman, M.: An analysis and survey of the development of mutation testing. IEEE Trans. Software Eng. 37(5), 649\u2013678 (2011)","journal-title":"IEEE Trans. Software Eng."},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Shen, W., Wan, J., Chen, Z.: Munn: mutation analysis of neural networks. In: 2018 IEEE International Conference on Software Quality, Reliability and Security Companion, QRS Companion 2018, Lisbon, Portugal, 16\u201320 July 2018, pp. 108\u2013115. IEEE (2018)","DOI":"10.1109\/QRS-C.2018.00032"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Wu, H., Li, Z., Cui, Z., Zhang, J.: A mutation-based approach to repair deep neural network models. In: 8th International Conference on Dependable Systems and Their Applications, DSA 2021, Yinchuan, China, 5\u20136 August 2021, pp. 730\u2013731. IEEE (2021)","DOI":"10.1109\/DSA52907.2021.00106"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Humbatova, N., Jahangirova, G., Tonella, P.: Deepcrime: mutation testing of deep learning systems based on real faults. In: ISSTA 2021: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, Denmark, 11\u201317 July 2021, pp. 67\u201378. ACM (2021)","DOI":"10.1145\/3460319.3464825"},{"key":"2_CR19","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371\u20133408 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"2_CR20","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 8\u201313 December 2014, Montreal, Quebec, Canada, pp. 2672\u20132680 (2014)"},{"key":"2_CR21","unstructured":"Lipton, R.: Fault diagnosis of computer programs. Ph.D. thesis, Carnegie Mellon University (1971)"},{"issue":"4","key":"2_CR22","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/C-M.1978.218136","volume":"11","author":"RA DeMillo","year":"1978","unstructured":"DeMillo, R.A., Lipton, R.J., Sayward, F.G.: Hints on test data selection: help for the practicing programmer. Computer 11(4), 34\u201341 (1978)","journal-title":"Computer"},{"issue":"4","key":"2_CR23","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1109\/TSE.1977.231145","volume":"3","author":"RG Hamlet","year":"1977","unstructured":"Hamlet, R.G.: Testing programs with the aid of a compiler. IEEE Trans. Software Eng. 3(4), 279\u2013290 (1977)","journal-title":"IEEE Trans. Software Eng."},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Jahangirova, G., Tonella, P.: An empirical evaluation of mutation operators for deep learning systems. In: 13th IEEE International Conference on Software Testing, Validation and Verification, ICST 2020, Porto, Portugal, 24\u201328 October 2020, pp. 74\u201384. IEEE (2020)","DOI":"10.1109\/ICST46399.2020.00018"},{"key":"2_CR25","unstructured":"MTUL, August 2022. https:\/\/github.com\/Yuteng-Lu\/MT-GAN"},{"issue":"9","key":"2_CR26","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TSE.2018.2809496","volume":"45","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Zhang, L., Harman, M., Hao, D., Jia, Y., Zhang, L.: Predictive mutation testing. IEEE Trans. Software Eng. 45(9), 898\u2013918 (2019)","journal-title":"IEEE Trans. Software Eng."},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"V\u00e1squez, M.L., et al.: Enabling mutation testing for android apps. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, ESEC\/FSE 2017, Paderborn, Germany, 4\u20138 September 2017, pp. 233\u2013244. ACM (2017)","DOI":"10.1145\/3106237.3106275"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Moran, K., et al.: MDroid+: a mutation testing framework for android. In: Proceedings of the 40th International Conference on Software Engineering: Companion Proceedings, ICSE 2018, Gothenburg, Sweden, 27 May\u201303 June 2018, pp. 33\u201336. ACM (2018)","DOI":"10.1145\/3183440.3183492"},{"issue":"5786","key":"2_CR29","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504\u2013507 (2006)","journal-title":"Science"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Semeniuta, S., Severyn, A., Barth, E.: A hybrid convolutional variational autoencoder for text generation. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9\u201311 September 2017, pp. 627\u2013637. Association for Computational Linguistics (2017)","DOI":"10.18653\/v1\/D17-1066"},{"key":"2_CR31","unstructured":"Jin, W., Barzilay, R., Jaakkola, T.S.: Junction tree variational autoencoder for molecular graph generation. In: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, 10\u201315 July 2018. Proceedings of Machine Learning Research, vol. 80, pp. 2328\u20132337. PMLR (2018)"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C.: Adversarially regularized graph autoencoder for graph embedding. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, 13\u201319 July 2018, Stockholm, Sweden, pp. 2609\u20132615. ijcai.org (2018)","DOI":"10.24963\/ijcai.2018\/362"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Kodirov, E., Xiang, T., Gong, S.: Semantic autoencoder for zero-shot learning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 4447\u20134456. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.473"}],"container-title":["Lecture Notes in Computer Science","Dependable Software Engineering. Theories, Tools, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21213-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T04:57:42Z","timestamp":1728536262000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21213-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031212123","9783031212130"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21213-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SETTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Dependable Software Engineering: Theories, Tools, and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"setta2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}