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Visual Text Mining: Ensuring the Presence of Relevant Studies in Systematic Literature Reviews
Federal Technological University of Paraná, Brazil.
University of São Paulo, Brazil.
University of São Paulo, Brazil.ORCID iD: 0000-0002-2901-935X
University of São Paulo, Brazil.
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2015 (English)In: International journal of software engineering and knowledge engineering, ISSN 0218-1940, Vol. 25, no 5, p. 909-928Article in journal (Refereed) Published
Abstract [en]

One of the activities associated with the Systematic Literature Review (SLR) process is the selection review of primary studies. When the researcher faces large volumes of primary studies to be analyzed, the process used to select studies can be arduous. In a previous experiment, we conducted a pilot test to compare the performance and accuracy of PhD students in conducting the selection review activity manually and using Visual Text Mining (VTM) techniques. The goal of this paper is to describe a replication study involving PhD and Master students. The replication study uses the same experimental design and materials of the original experiment. This study also aims to investigate whether the researcher's level of experience with conducting SLRs and research in general impacts the outcome of the primary study selection step of the SLR process. The replication results have con¯rmed the outcomes of the original experiment, i.e., VTM is promising and can improve the performance of the selection review of primary studies. We also observed that both accuracy and performance increase in function of the researcher's experience level in conducting SLRs. The use of VTM can indeed be beneficial during the selection review activity.

Place, publisher, year, edition, pages
World Scientific, 2015. Vol. 25, no 5, p. 909-928
Keywords [en]
Systematic literature review, visual text mining
National Category
Computer Sciences
Research subject
Computer Science, Information and software visualization; Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-73234DOI: 10.1142/S0218194015500114OAI: oai:DiVA.org:lnu-73234DiVA, id: diva2:1199776
Available from: 2018-04-22 Created: 2018-04-22 Last updated: 2018-04-26Bibliographically approved

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Martins, Rafael Messias

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf