Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/25998
Autoria: | Cairo, L. Monteiro, M. P. Carneiro, G. de F. Brito e Abreu, F. |
Data: | 2019 |
Título próprio: | Towards the use of machine learning algorithms to enhance the effectiveness of search strings in secondary studies |
Título e volume do livro: | Proceedings of the XXXIII Brazilian Symposium on Software Engineering |
Paginação: | 22 - 26 |
Título do evento: | 33rd Brazilian Symposium on Software Engineering, SBES 2019 |
ISBN: | 978-145037651-8 |
DOI (Digital Object Identifier): | 10.1145/3350768.3350772 |
Palavras-chave: | Secondary studies Machine learning Natural language processing |
Resumo: | Devising an appropriate Search String for a secondary study is not a trivial task and identifying suitable keywords has been reported in the literature as a difficulty faced by researchers. A poorly chosen Search String may compromise the quality of the secondary study, by missing relevant studies or leading to overwork in subsequent steps of the secondary study, in case irrelevant studies are selected. In this paper, we propose an approach for the creation and calibration of a Search String. We chose three published systematic literature reviews (SLRs) from Scopus and applied Machine Learning algorithms to create the corresponding Search Strings to be used in the SLRs. Comparison of results obtained with those published in previous SLRs, show an increase of recall of revisions by up to 12%, with no loss of recall. To motivate future studies and replications, the tool implementing the proposed approach is available in a public repository, along with the dataset used in this paper. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | ISTAR-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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conferenceobject_67150.pdf | 265,71 kB | Adobe PDF | Ver/Abrir |
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