{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T20:52:01Z","timestamp":1777409521633,"version":"3.51.4"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030312794","type":"print"},{"value":"9783030312800","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-31280-0_4","type":"book-chapter","created":{"date-parts":[[2019,10,7]],"date-time":"2019-10-07T23:06:50Z","timestamp":1570489610000},"page":"56-64","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Generating Biased Dataset for Metamorphic Testing of Machine Learning Programs"],"prefix":"10.1007","author":[{"given":"Shin","family":"Nakajima","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tsong Yueh","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,8]]},"reference":[{"key":"4_CR1","unstructured":"Chen, T.Y., Chung, S.C., Yiu, S.M.: Metamorphic Testing - A New Approach for Generating Next Test Cases, HKUST-CS98-01, The Hong Kong University of Science and Technology (1998)"},{"issue":"1","key":"4_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3143561","volume":"51","author":"TY Chen","year":"2018","unstructured":"Chen, T.Y., et al.: Metamorphic testing: a review of challenges and opportunities. ACM Comput. Surv. 51(1), 1\u201327 (2018). Article No. 4","journal-title":"ACM Comput. Surv."},{"key":"4_CR3","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances NIPS 2014, pp. 2672\u20132680 (2014)"},{"key":"4_CR4","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. The MIT Press, Cambridge (2016)"},{"key":"4_CR5","unstructured":"Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Pearson India (2016)"},{"issue":"1","key":"4_CR6","doi-asserted-by":"publisher","first-page":"153","DOI":"10.2307\/1912352","volume":"47","author":"JJ Heckman","year":"1979","unstructured":"Heckman, J.J.: Selection bias as a specification error. Econometrica 47(1), 153\u2013161 (1979)","journal-title":"Econometrica"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Nakajima, S., Bui, H.N.: Dataset coverage for testing machine learning computer programs. In: Proceedings of the 23rd APSEC, pp. 297\u2013304 (2016)","DOI":"10.1109\/APSEC.2016.049"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Nakajima, S.: Quality assurance of machine learning software. In: Proceedings GCCE 2018, pp. 601\u2013604 (2018)","DOI":"10.1109\/GCCE.2018.8574766"},{"key":"4_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-030-13651-2_2","volume-title":"Structured Object-Oriented Formal Language and Method","author":"S Nakajima","year":"2019","unstructured":"Nakajima, S.: Dataset diversity for metamorphic testing of machine learning software. In: Duan, Z., Liu, S., Tian, C., Nagoya, F. (eds.) SOFL+MSVL 2018. LNCS, vol. 11392, pp. 21\u201338. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-13651-2_2"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Pei, K., Cao, Y., Yang, J., Jana, S.: DeepXplore: automated whitebox testing of deep learning systems. In: Proceedings of the 26th SOSP, pp. 1\u201318 (2017)","DOI":"10.1145\/3132747.3132785"},{"key":"4_CR11","volume-title":"Dataset Shift in Machine Learning","year":"2009","unstructured":"Quinonero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D. (eds.): Dataset Shift in Machine Learning. The MIT Press, Cambridge (2009)"},{"key":"4_CR12","unstructured":"Segura, S., Towey, D., Zhou, Z.Q., Chen, T.Y.: Metamorphic testing: testing the untestable. IEEE Softw. (in press)"},{"key":"4_CR13","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: Proceedings of the ICLR 2014 (2014)"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Warde-Farley, D., Goodfellow, I.: Adversarial perturbations of deep neural networks. In: Perturbation, Optimization and Statistics, pp. 1\u201332. The MIT Press, Cambridge (2016)","DOI":"10.7551\/mitpress\/10761.003.0012"},{"issue":"4","key":"4_CR15","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.jss.2010.11.920","volume":"84","author":"X Xie","year":"2011","unstructured":"Xie, X., Ho, J.W.K., Murphy, C., Kaiser, G., Xu, B., Chen, T.Y.: Testing and validating machine learning classifiers by metamorphic testing. J. Syst. Softw. 84(4), 544\u2013558 (2011)","journal-title":"J. Syst. Softw."},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, Y., Zhang, L., Liu, C., Khurshid, S.: DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems. In: Proceedings of the 33rd ASE, pp. 132\u2013142 (2018)","DOI":"10.1145\/3238147.3238187"},{"issue":"3","key":"4_CR17","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1145\/3241979","volume":"62","author":"ZQ Zhou","year":"2019","unstructured":"Zhou, Z.Q., Sun, L.: Metamorphic testing of driverless cars. Comm. ACM 62(3), 61\u201367 (2019)","journal-title":"Comm. ACM"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Zhu, X.: Machine teaching: an inverse problem to machine learning and an approach toward optimal education. In: Proceedings of the 29th AAAI, pp. 4083\u20134087 (2015)","DOI":"10.1609\/aaai.v29i1.9761"}],"container-title":["Lecture Notes in Computer Science","Testing Software and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-31280-0_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:58:21Z","timestamp":1710363501000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-31280-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030312794","9783030312800"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-31280-0_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"8 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICTSS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Testing Software and Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Paris","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pts2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ictss2019.centralesupelec.fr\/","order":11,"name":"conference_url","label":"Conference URL","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":"30","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":"14","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":"47% - 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":"3","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":"2,1","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}