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@InProceedings{FelizardoMenKalSouVij:2016:UsFoSn,
               author = "Felizardo, Katia Romero and Mendes, Em{\'{\i}}lia and 
                         Kalinowski, Marcos and Souza, {\'E}rica Ferreira and Vijaykumar, 
                         Nandamudi Lankalapalli",
          affiliation = "{Universidade Federal Tecnol{\'o}gico do Paran{\'a} (UFTPR)} and 
                         {Blekinge Institute of Technology} and {Universidade Federal 
                         Fluminense (UFF)} and {Universidade Federal Tecnol{\'o}gico do 
                         Paran{\'a} (UFTPR)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Using forward snowballing to update systematic reviews in software 
                         engineering",
            booktitle = "Proceedings...",
                 year = "2016",
         organization = "International Symposium on Empirical Software Engineering and 
                         Measurement",
             keywords = "forward snowballing, Systematic literature reviews.",
             abstract = "Background: A Systematic Literature Review (SLR) is a methodology 
                         used to aggregate relevant evidence related to one or more 
                         research questions. Whenever new evidence is published after the 
                         completion of a SLR, this SLR should be updated in order to 
                         preserve its value. However, updating SLRs involves significant 
                         effort. Objective: The goal of this paper is to investigate the 
                         application of forward snowballing to support the update of SLRs. 
                         Method: We compare outcomes of an update achieved using the 
                         forward snowballing versus a published update using the 
                         search-based approach, i.e., searching for studies in electronic 
                         databases using a search string. Results: Forward snowballing 
                         showed a higher precision and a slightly lower recall. It reduced 
                         in more than five times the number of primary studies to filter 
                         however missed one relevant study. Conclusions: Due to its high 
                         precision, we believe that the use of forward snowballing 
                         considerably reduces the effort in updating SLRs in Software 
                         Engineering; however the risk of missing relevant papers should 
                         not be underrated.",
  conference-location = "Ciudad Real, Espanha",
      conference-year = "2016",
             language = "en",
        urlaccessdate = "27 nov. 2020"
}


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