• DocumentCode
    3368649
  • Title

    Comparison of two models of success prediction in software development projects

  • Author

    Maglyas, Andrey ; Nikula, Uolevi ; Smolander, Kari

  • Author_Institution
    Lappeenranta Univ. of Technol., Lappeenranta, Finland
  • fYear
    2010
  • fDate
    13-15 Oct. 2010
  • Firstpage
    43
  • Lastpage
    49
  • Abstract
    Background: The size and complexity of software development projects are growing. At the same time, the proportion of successful projects is still quite low according to the previous research. One way to approach this problem is to develop and use methods that can predict project success beforehand and act accordingly. Aim: The objective of this study is to compare two existing models of success prediction (The Standish Group and McConnell models) and to determine their strengths and weaknesses. Method: The research was done as an empirical study. A survey with structured forms and theme-based interviews were used as the data collection methods. The comparison is made with observations from 48 projects in Russia, Belarus, and Ukraine. In addition, 19 interviews were conducted during the study. Conclusions: The results show that The Standish Group has a tendency to overestimate the problems in a project. McConnell predicts successful projects pretty well but underestimates the percentage of unsuccessful projects.
  • Keywords
    project management; software development management; The Standish Group; data collection method; software development project; success prediction; theme based interview; Biological system modeling; Companies; Interviews; Predictive models; Programming; Schedules; Software; McConnell model; Standish model; success prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering Conference (CEE-SECR), 2010 6th Central and Eastern European
  • Conference_Location
    Moscow
  • Print_ISBN
    978-1-4577-0605-9
  • Type

    conf

  • DOI
    10.1109/CEE-SECR.2010.5783149
  • Filename
    5783149