Advertisement

An Empirical Evaluation of Predicting Runaway Software Projects Using Bayesian Classification

  • Osamu Mizuno
  • Takanari Hamasaki
  • Yasunari Takagi
  • Tohru Kikuno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3009)

Abstract

Since software development projects often fall into runaway situations, detecting signs of runaway status in early stage of development has become important. In this paper, we propose a new scheme for the prediction of runaway projects based on an empirical questionnaire. We first design a questionnaire from five viewpoints within the projects: requirements, estimations, planning, team organization, and project management activities. Each of these viewpoints consists of questions in which experience and knowledge of software risks are included. Secondly, we classify projects into “runaway” and “success” using resultant metrics data. We then analyze the relationship between responses to the questionnaire and the runaway status of projects by the Bayesian classification. The experimental result using actual project data shows that 33 out of 40 projects were predicted correctly. As a result, we confirm that the prediction of runaway projects is successful.

Keywords

Empirical Evaluation Project Member Software Development Project Software Process Improvement Team Organization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Boehm, B.W.: Industrial software metrics top 10 list. IEEE Software 4, 84–85 (1987)MathSciNetGoogle Scholar
  2. 2.
    Jiang, J., Klein, G.: Software development risks to project effectiveness. Journal of Systems and Software 52, 3–10 (2000)CrossRefGoogle Scholar
  3. 3.
    Wohlin, C., Andrews, A.A.: Prioritizing and assessing software project success factors and project characteristics using subjective data. Empirical Software Engineering 8, 285–303 (2003)CrossRefGoogle Scholar
  4. 4.
    Williams, R.C., Pandelios, G.J., Behrens, S.G.: Software risk evaluation (SRE) method description (version 2.0). Technical Report CMU/SEI-99-TR-029, Software Engineering Institute (1999)Google Scholar
  5. 5.
    Conrow, E.H., Shishido, P.S.: Implementing risk management on software intensive projects. IEEE Software 14, 83–89 (1997)CrossRefGoogle Scholar
  6. 6.
    Fairley, R., Rook, P.: Risk management for software development. In: Software Engineering, pp. 387–400. IEEE CS Press, Los Alamitos (1997)Google Scholar
  7. 7.
    Karolak, D.W.: Software Engineering Risk Management. IEEE CS Press, CA (1996)Google Scholar
  8. 8.
    Sisti, F.J., Joseph, S.: Software risk evaluation method version 1.0. Technical Report CMU/SEI-94-TR-19, Software Engineering Institute (1994)Google Scholar
  9. 9.
    Humphrey, W.S.: A Discipline for Software Engineering. Addison-Wesley, MA (1995)Google Scholar
  10. 10.
    Yourdon, E.: Death March: The Complete Software Developer’s Guide to Surviving ‘Mission Impossible’ Projects. Prentice-Hall Computer Books, Englewood Cliffs (1997)Google Scholar
  11. 11.
    Fenton, N.E., Pfleeger, S.L.: Software Metrics: A Rigorous & Practical Approach. PWS Publishing (1997)Google Scholar
  12. 12.
    Mizuno, O., Kikuno, T., Takagi, Y., Sakamoto, K.: Characterization of risky projects based on project managers’ evaluation. In: Proc. of 22nd International Conference on Software Engineering, pp. 387–395 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Osamu Mizuno
    • 1
  • Takanari Hamasaki
    • 1
  • Yasunari Takagi
    • 2
  • Tohru Kikuno
    • 1
  1. 1.Osaka UniversityToyonaka, OsakaJapan
  2. 2.OMRON CorporationKusatsu, ShigaJapan

Personalised recommendations