@InProceedings{FelizardoSouLopMorVij:2020:SyMaSu,
               author = "Felizardo, K{\'a}tia R. and Souza, {\'E}rica F. de and Lopes, 
                         Rafael and Moro, Geovanne J. and Vijaykumar, Nandamudi 
                         Lankalapalli",
          affiliation = "{Universidade Tecnol{\'o}gica Federal do Paran{\'a} (UTFPR)} and 
                         {Universidade Tecnol{\'o}gica Federal do Paran{\'a} (UTFPR)} and 
                         {Universidade Tecnol{\'o}gica Federal do Paran{\'a} (UTFPR)} and 
                         {Universidade Tecnol{\'o}gica Federal do Paran{\'a} (UTFPR)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Crowdsourcing in Systematic Reviews: A Systematic Mapping and 
                         Survey",
            booktitle = "Proceedings...",
                 year = "2020",
         organization = "Euromicro Conference on Software Engineering and Advanced 
                         Applications, 46.",
            publisher = "IEEE",
             keywords = "Systematic Review, SR, Systematic Mapping, SM, Crowdsourcing.",
             abstract = "Context:Systematic reviews (SRs) have been adopted in the Software 
                         Engineering (SE) field for more than a decade to provide synthesis 
                         of evidence on various topics. However, the process in conducting 
                         an SR remains laborious-intensive and expensive, specially in 
                         terms of hours that SR researchers dedicate. It is worth exploring 
                         approaches to conduct SRs at lower costs (quicker, using less 
                         resources time of researchers). One such approach is 
                         crowdsourcing, since conducting SRs activities among a large 
                         number of researchers is a promising alternative to reduce costs 
                         associated to SR conduction. Goal: The main goal of this study is 
                         to identify and summarize the body of knowledge on crowdsourcing 
                         to support the conduction of SRs in SE. Method: Two empirical 
                         research methods were used. Initially, we conducted a Systematic 
                         Mapping to identify the available and relevant studies on 
                         crowdsourcing in SRs in SE. Secondly, a survey was performed with 
                         39 SE researchers aiming to identify their perception related to 
                         the value of performing SRs collaboratively. Results: Our results 
                         show that how to speed up the SR process; reduce bias through 
                         broad participation; and expand team expertise were most potential 
                         benefits linked to the use of crowdsourcing in SR. The main 
                         challenges were associated with quality control to ensure the 
                         quality of results. Conclusions: In spite of the challenges, we 
                         believe that crowdsourcing could be successfully employed in SR 
                         context. More empirical research is needed on how to use 
                         crowdsourcing to support SR conduction in SE and how to minimize 
                         the identified challenges.",
  conference-location = "Kranj, Slovenia",
      conference-year = "26-28 Aug.",
                  doi = "10.1109/SEAA51224.2020.00072",
                  url = "http://dx.doi.org/10.1109/SEAA51224.2020.00072",
                 isbn = "978-172819532-2",
           targetfile = "felizardo_crowd.pdf",
        urlaccessdate = "27 abr. 2024"
}