Improving Testing of Deep-Learning Systems.
The article focuses on the improvement of test effectiveness and quality of test data in deep learning systems. Topics of discussion include the combination of mutation testing using DeepMutation and differential testing using DeepXplore, iterations of models using data from the MNIST database, and...
| Published in: | Communications of the ACM Vol. 67; no. 3; pp. 44 - 49 |
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| Main Authors: | , , , |
| Format: | Article |
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Association for Computing Machinery
Mar2024
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=175599204&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 175599204 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Mar2024 vid: 67 iid: 3 pid: 68 pub: Association for Computing Machinery artinfo: ui: 175599204 10.1145/3633311 ppf: 44 ppct: 5 formats: tig: atl: Improving Testing of Deep-Learning Systems. aug: au: Deokuliar, Harsh Sangwan, Raghvinder S. Badr, Yoaukim Srinivasan, Satish M. affil: Pennsylvania State University, School of Graduate Studies, Malvern, PA, USA Carnegie Mellon University, Pittsburgh, PA, USA Pennsylvania State University Great Valley, Malvern, PA, USA su: Deep learning Computer software testing Data quality Mutation testing of computer software Iterative methods (Mathematics) Artificial neural networks sug: subj: Deep learning Computer software testing Data quality Mutation testing of computer software Iterative methods (Mathematics) Artificial neural networks ab: The article focuses on the improvement of test effectiveness and quality of test data in deep learning systems. Topics of discussion include the combination of mutation testing using DeepMutation and differential testing using DeepXplore, iterations of models using data from the MNIST database, and the discovery of errors in deep neural network (DNN) models. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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