• DocumentCode
    261862
  • Title

    A Participant Recruitment Framework for Crowdsourcing Based Software Requirement Acquisition

  • Author

    Hao Wang ; Yasha Wang ; Jiangtao Wang

  • Author_Institution
    Key Lab. of High-Confidence Software Technol., Beijing, China
  • fYear
    2014
  • fDate
    18-21 Aug. 2014
  • Firstpage
    65
  • Lastpage
    73
  • Abstract
    The opportunity to leverage crowd sourcing-based model to facilitate software requirements acquisition has been recognized to maximize the advantages of the diversity of talents and expertise available within the crowd. Identifying well-suited participants is a common issue in crowd sourcing system. Requirements acquisition tasks call for participants with particular kind of domain knowledge. However, current crowd sourcing system failed to provide such kind of identification among participants. We observed that participants with a particular kind of domain knowledge often have the opportunity to cluster in particular spatiotemporal spaces. Based on this observation, we propose a novel opportunistic participant recruitment framework to enable organizers to recruit participants with desired kind of domain knowledge in a more efficient way. We analyzed the feasibility of our opportunistic approach through both theoretic study on analytical model and simulated experiment on real world mobility model. The results showed the feasibility of our approach.
  • Keywords
    information retrieval; software engineering; crowdsourcing based software requirement acquisition; domain knowledge; opportunistic participant recruitment framework; real world mobility model; requirements acquisition task; spatiotemporal space; Analytical models; Crowdsourcing; Educational institutions; Peer-to-peer computing; Recruitment; Software; Spatiotemporal phenomena; Crowdsourcing; Opportunistic Network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Global Software Engineering (ICGSE), 2014 IEEE 9th International Conference on
  • Conference_Location
    Shanghai
  • Type

    conf

  • DOI
    10.1109/ICGSE.2014.26
  • Filename
    6915255