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1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemtc-m21d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34T/47833QH
Repositóriosid.inpe.br/mtc-m21d/2022/07.05.14.13   (acesso restrito)
Última Atualização2022:07.05.14.13.35 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2022/07.05.14.13.35
Última Atualização dos Metadados2023:01.03.16.46.09 (UTC) administrator
Chave SecundáriaINPE--PRE/
Chave de CitaçãoSantiagoJúnior:2022:MeExEv
TítuloA Method and Experiment to evaluate Deep Neural Networks as Test Oracles for Scientific Software
Ano2022
Data de Acesso30 jun. 2024
Tipo SecundárioPRE CI
Número de Arquivos1
Tamanho738 KiB
2. Contextualização
AutorSantiago Júnior, Valdivino Alexandre de
Identificador de Curriculo8JMKD3MGP5W/3C9JJB5
GrupoCOPDT-CGIP-INPE-MCTI-GOV-BR
AfiliaçãoInstituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autorvaldivino.santiago@inpe.br
Nome do EventoIEEE/ACM International Conference on Automation of Software Test (AST)
Data16-20 May 2022
Editora (Publisher)IEEE
Título do LivroProceedings
Histórico (UTC)2022-07-05 14:14:08 :: simone -> administrator :: 2022
2022-07-12 02:05:05 :: administrator -> simone :: 2022
2022-12-20 13:10:45 :: simone -> administrator :: 2022
2023-01-03 16:46:09 :: administrator -> simone :: 2022
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveTest Oracles
Deep Convolutional Neural Networks
Transfer Learning

Explainable Artificial Intelligence
Data-Centric Artificial Intelligenc
ResumoTesting scientific software is challenging because usually such type of systems have non-deterministic behaviours and, in addition, they generate non-trivial outputs such as images. Artificial intelligence (AI) is now a reality which is also helping in the development of the software testing activity. In this article, we evaluate seven deep neural networks (DNNs), precisely deep convolutional neural networks (CNNs) with up to 161 layers, playing the role of test oracle procedures for testing scientific models. Firstly, we propose a method, TOrC, which starts by generating training, validation, and test image datasets via combinatorial interaction testing applied to the original codes and second-order mutants. Within TOrC we also have classical steps such as transfer learning, a technique recommended for DNNs. Then, we verified the performance of the oracles (CNNs). The main conclusions of this research are: i) not necessarily a greater number of layers means that a CNN will present better performance; ii) transfer learning is a valuable technique but eventually we may need extended solutions to get better performances; iii) data-centric AI is an interesting path to follow; and iv) there is not a clear correlation between the software bugs, in the scientific models, and the errors (image misclassifications) presented by the CNNs.
ÁreaCOMP
Arranjourlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > A Method and...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 05/07/2022 11:13 1.0 KiB 
4. Condições de acesso e uso
Idiomaen
Arquivo AlvoPaper 2_A Method_Oficial.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/46KUES5
Lista de Itens Citandosid.inpe.br/bibdigital/2022/04.03.23.11 2
sid.inpe.br/mtc-m21/2012/07.13.15.01.24 2
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
Campos Vaziosarchivingpolicy archivist callnumber conferencelocation copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisheraddress rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url volume
7. Controle da descrição
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