Towards the use of machine learning algorithms to enhance the effectiveness of search strings in secondary studies

Leonardo Cairo, Miguel P. Monteiro, Glauco de F. Carneiro, Fernando Brito E Abreu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 33rd Brazilian Symposium on Software Engineering, SBES 2019
PublisherAssociation for Computing Machinery
Pages22-26
Number of pages5
ISBN (Electronic)9781450376518
DOIs
Publication statusPublished - 23 Sep 2019
Event33rd Brazilian Symposium on Software Engineering, SBES 2019 - Salvador, Brazil
Duration: 23 Sep 201927 Sep 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference33rd Brazilian Symposium on Software Engineering, SBES 2019
Country/TerritoryBrazil
CitySalvador
Period23/09/1927/09/19

Keywords

  • Machine learning
  • Natural language processing
  • Secondary studies

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