{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T05:29:52Z","timestamp":1747805392707,"version":"3.37.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030205201"},{"type":"electronic","value":"9783030205218"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-20521-8_63","type":"book-chapter","created":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T19:02:40Z","timestamp":1559674960000},"page":"766-777","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Investigating the Effectiveness of Mutation Testing Tools in the Context of Deep Neural Networks"],"prefix":"10.1007","author":[{"given":"Nour","family":"Chetouane","sequence":"first","affiliation":[]},{"given":"Lorenz","family":"Klampfl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0462-2283","authenticated-orcid":false,"given":"Franz","family":"Wotawa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"key":"63_CR1","doi-asserted-by":"crossref","unstructured":"Coles, H., Laurent, T., Henard, C., Papadakis, M., Ventresque, A.: PIT: a practical mutation testing tool for Java. In: Proceedings of the 25th International Symposium on Software Testing and Analysis, pp. 449\u2013452. ACM (2016)","DOI":"10.1145\/2931037.2948707"},{"issue":"4","key":"63_CR2","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. IEEE Comput. 11(4), 34\u201341 (1978)","journal-title":"IEEE Comput."},{"key":"63_CR3","doi-asserted-by":"crossref","unstructured":"Eykholt, K., et al.: Robust physical-world attacks on deep learning visual classification. In: Proceedings CVPR (2018). \n                      arXiv: 1707.08945v5","DOI":"10.1109\/CVPR.2018.00175"},{"key":"63_CR4","unstructured":"Gibson, A., et al.: Deeplearning4j: distributed, open-source deep learning for Java and Scala on Hadoop and Spark (2016)"},{"key":"63_CR5","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Erkan, A., Sermanet, P., Scoffier, M., Muller, U., LeCun, Y.: Deep belief net learning in a long-range vision system for autonomous off-road driving. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France, September 2008","DOI":"10.1109\/IROS.2008.4651217"},{"key":"63_CR6","unstructured":"Huval, B., et al.: An empirical evaluation of deep learning on highway driving (2015). \n                      arXiv: 1504.01716v3"},{"key":"63_CR7","doi-asserted-by":"crossref","unstructured":"Just, R.: The major mutation framework: efficient and scalable mutation analysis for Java. In: Proceedings of the 2014 International Symposium on Software Testing and Analysis, pp. 433\u2013436. ACM (2014)","DOI":"10.1145\/2610384.2628053"},{"key":"63_CR8","unstructured":"LeCun, Y.: The MNIST database of handwritten digits (1998). \n                      http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"63_CR9","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, pp. 120\u2013131. ACM (2018)","DOI":"10.1145\/3238147.3238202"},{"key":"63_CR10","doi-asserted-by":"crossref","unstructured":"Ma, L., 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":"63_CR11","unstructured":"Ma, L., et al.: Combinatorial testing for deep learning systems. arXiv preprint \n                      arXiv:1806.07723\n                      \n                     (2018)"},{"key":"63_CR12","doi-asserted-by":"crossref","unstructured":"Ma, Y.S., Offutt, J., Kwon, Y.R.: MuJava: a mutation system for Java. In: Proceedings of the 28th International Conference on Software Engineering, pp. 827\u2013830. ACM (2006)","DOI":"10.1145\/1134285.1134425"},{"issue":"1","key":"63_CR13","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/TSE.2013.44","volume":"40","author":"L Madeyski","year":"2014","unstructured":"Madeyski, L., Orzeszyna, W., Torkar, R., J\u00f2zala, M.: Overcoming the equivalent mutant problem: a systematic literature review and a comparative experiment of second order mutation. IEEE Trans. Softw. Eng. 40(1), 23\u201342 (2014)","journal-title":"IEEE Trans. Softw. Eng."},{"issue":"1","key":"63_CR14","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1145\/125489.125473","volume":"1","author":"AJ Offutt","year":"1992","unstructured":"Offutt, A.J.: Investigations of the software testing coupling effect. ACM Trans. Softw. Eng. Method. 1(1), 5\u201320 (1992)","journal-title":"ACM Trans. Softw. Eng. Method."},{"key":"63_CR15","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 Symposium on Operating Systems Principles, pp. 1\u201318. ACM (2017)","DOI":"10.1145\/3132747.3132785"},{"issue":"19","key":"63_CR16","doi-asserted-by":"publisher","first-page":"70","DOI":"10.2352\/ISSN.2470-1173.2017.19.AVM-023","volume":"2017","author":"AhmadEL Sallab","year":"2017","unstructured":"Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. In: Proceedings IS&T International Symposium on Electronic Imaging, Autonomous Vehicles and Machines. Society for Imaging Science and Technology (2017). \n                      https:\/\/doi.org\/10.2352\/ISSN.2470-1173.2017.19.AVM-023","journal-title":"Electronic Imaging"},{"key":"63_CR17","doi-asserted-by":"crossref","unstructured":"Sun, Y., Huang, X., Kroening, D.: Testing deep neural networks. arXiv preprint \n                      arXiv:1803.04792\n                      \n                     (2018)","DOI":"10.1145\/3238147.3238172"},{"key":"63_CR18","doi-asserted-by":"publisher","unstructured":"Tian, Y., Pei, K., Jana, S., Ray, B.: DeepTest: automated testing of deep-neural-network-driven autonomous cars. In: Proceedings of the ACM\/IEEE 40th International Conference on Software Engineering. ACM, New York, Gothenburg, Sweden, May\u2013June 2018. \n                      https:\/\/doi.org\/10.1145\/3180155.3180220","DOI":"10.1145\/3180155.3180220"},{"key":"63_CR19","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques","author":"IH Witten","year":"2016","unstructured":"Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20521-8_63","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T19:09:29Z","timestamp":1559675369000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20521-8_63"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030205201","9783030205218"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20521-8_63","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gran Canaria","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"12 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwann.uma.es\/","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"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"210","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"150","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"71% - 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"}},{"value":"2,9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}