Fechar
Metadados

1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/43BQLBH
Repositóriosid.inpe.br/mtc-m21c/2020/10.02.16.01   (acesso restrito)
Última Atualização2020:10.02.16.01.04 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21c/2020/10.02.16.01.04
Última Atualização dos Metadados2022:01.04.01.35.26 (UTC) administrator
DOI10.1016/j.infsof.2020.106395
ISSN0950-5849
Chave de CitaçãoWatanabeFeCaSoCaVi:2020:ReEfSo
TítuloReducing efforts of software engineering systematic literature reviews updates using text classification
Ano2020
MêsDec.
Data de Acesso01 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho1242 KiB
2. Contextualização
Autor1 Watanabe, Willian Massami
2 Felizardo, Katia Romero
3 Candido Júnior, Arnaldo Candido
4 Souza, Érica Ferreira de
5 Campos Neto, José Ede de
6 Vijaykumar, Nandamudi Lankalapalli
Identificador de Curriculo1
2
3
4
5
6 8JMKD3MGP5W/3C9JHTU
Grupo1
2
3
4
5
6 LABAC-COCTE-INPE-MCTIC-GOV-BR
Afiliação1 Universidade Tecnológica Federal do Paraná (UTFPR)
2 Universidade Tecnológica Federal do Paraná (UTFPR)
3 Universidade Tecnológica Federal do Paraná (UTFPR)
4 Universidade Tecnológica Federal do Paraná (UTFPR)
5 Universidade Tecnológica Federal do Paraná (UTFPR)
6 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 wwatanabe@utfpr.edu.br
2 katiascannavino@utfpr.edu.br
3 arnaldoc@utfpr.edu.br
4 ericasouza@utfpr.edu.br
5
6 vijay.nl@inpe.br
RevistaInformation and Software Technology
Volume128
Páginase106395
Nota SecundáriaA2_MEDICINA_I A2_CIÊNCIA_DA_COMPUTAÇÃO B1_INTERDISCIPLINAR B2_SOCIOLOGIA
Histórico (UTC)2020-10-02 16:01:58 :: simone -> administrator :: 2020
2022-01-04 01:35:26 :: administrator -> simone :: 2020
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-ChaveSystematic literature review SLR Automatic selection Review update Text classification Document classification Text categorization
ResumoContext: Systematic Literature Reviews (SLRs) are frequently used to synthesize evidence in Software Engineering (SE), however replicating and keeping SLRs up-to-date is a major challenge. The activity of studies selection in SLR is labor intensive due to the large number of studies that must be analyzed. Different approaches have been investigated to support SLR processes, such as: Visual Text Mining or Text Classification. But acquiring the initial dataset is time-consuming and labor intensive. Objective: In this work, we proposed and evaluated the use of Text Classification to support the studies selection activity of new evidences to update SLRs in SE. Method: We applied Text Classification techniques to investigate how effective and how much effort could be spared during the studies selection phase of an SLR update. Considering the SLRs update scenario, the studies analyzed in the primary SLR could be used as a classified dataset to train Supervised Machine Learning algorithms. We conducted an experiment with 8 Software Engineering SLRs. In the experiments, we investigated the use of multiple preprocessing and feature extraction tasks such as tokenization, stop words removal, word lemmatization, TF-IDF (Term-Frequency/Inverse-Document-Frequency) with Decision Tree and Support Vector Machines as classification algorithms. Furthermore, we configured the classifier activation threshold for maximizing Recall, hence reducing the number of Missed selected studies. Results: The techniques accuracies were measured and the results achieved on average a F-Score of 0.92 and 62% of exclusion rate when varying the activation threshold of the classifiers, with a 4% average number of Missed selected studies. Both the Exclusion rate and number of Missed selected studies were significantly different when compared to classifier which did not use the configuration of the activation threshold. Conclusion: The results showed the potential of the techniques in reducing the effort required of SLRs updates.
ÁreaCOMP
Arranjourlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Reducing efforts of...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 02/10/2020 13:01 1.0 KiB 
4. Condições de acesso e uso
Idiomaen
Arquivo Alvowatanabe_reducing.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/3ESGTTP
Lista de Itens Citandosid.inpe.br/bibdigital/2013/09.22.23.14 1
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosalternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
e-Mail (login)simone
atualizar 


Fechar