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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Cairo, L., Monteiro, M. P., Carneiro, G. de F. & Brito e Abreu, F. (2019). Towards the use of machine learning algorithms to enhance the effectiveness of search strings in secondary studies. In Proceedings of the XXXIII Brazilian Symposium on Software Engineering. (pp. 22-26). Salvador, Brazil: ACM Press.
Exportar Referência (IEEE)
L. Cairo et al.,  "Towards the use of machine learning algorithms to enhance the effectiveness of search strings in secondary studies", in Proc. of the XXXIII Brazilian Symp. on Software Engineering, Salvador, Brazil, ACM Press, 2019, pp. 22-26
Exportar BibTeX
@inproceedings{cairo2019_1708496217882,
	author = "Cairo, L. and Monteiro, M. P. and Carneiro, G. de F. and Brito e Abreu, F.",
	title = "Towards the use of machine learning algorithms to enhance the effectiveness of search strings in secondary studies",
	booktitle = "Proceedings of the XXXIII Brazilian Symposium on Software Engineering",
	year = "2019",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1145/3350768.3350772",
	pages = "22-26",
	publisher = "ACM Press",
	address = "Salvador, Brazil",
	organization = "Sociedade Brasileira de Computação (SBC)",
	url = "https://dl.acm.org/doi/proceedings/10.1145/3350768"
}
Exportar RIS
TY  - CPAPER
TI  - Towards the use of machine learning algorithms to enhance the effectiveness of search strings in secondary studies
T2  - Proceedings of the XXXIII Brazilian Symposium on Software Engineering
AU  - Cairo, L.
AU  - Monteiro, M. P.
AU  - Carneiro, G. de F.
AU  - Brito e Abreu, F.
PY  - 2019
SP  - 22-26
DO  - 10.1145/3350768.3350772
CY  - Salvador, Brazil
UR  - https://dl.acm.org/doi/proceedings/10.1145/3350768
AB  - 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.
ER  -