How Healthy Is My Project? Open Source Project Attributes as Indicators of Success

  • James Piggot
  • Chintan Amrit
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 404)

Abstract

Determining what factors can influence the successful outcome of a software project has been labeled by many scholars and software engineers as a difficult problem. In this paper we use machine learning to create a model that can determine the stage a software project has obtained with some accuracy. Our model uses 8 Open Source project metrics to determine the stage a project is in. We validate our model using two performance measures; the exact success rate of classifying an Open Source Software project and the success rate over an interval of one stage of its actual performance using different scales of our dependent variable. In all cases we obtain an accuracy of above 70% with one away classification (a classification which is away by one) and about 40% accuracy with an exact classification. We also determine the factors (according to one classifier) that uses only eight variables among all the variables available in SourceForge, that determine the health of an OSS project.

References

  1. 1.
    DeLone, W.H., McLean, E.R.: The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems 19, 9–30 (2003)Google Scholar
  2. 2.
    Subramaniam, C., et al.: Determinants of open source software project success: A longitudinal study. Decision Support Systems 46, 576–585 (2009)CrossRefGoogle Scholar
  3. 3.
    Crowston, K., et al.: Information systems success in free and open source software development: theory and measures. Software Process Improvement and Practice 11, 123–148 (2006)CrossRefGoogle Scholar
  4. 4.
    Comino, S., et al.: From planning to mature: On the success of open source projects. Research Policy 36, 1575–1586 (2007)CrossRefGoogle Scholar
  5. 5.
    Lee, S.Y.T., et al.: Measuring open source software success. Omega 37, 426–438 (2009)CrossRefGoogle Scholar
  6. 6.
    Midha, V., Palvia, P.: Factors affecting the success of Open Source Software. Journal of Systems and Software (2011)Google Scholar
  7. 7.
    Snow, A.P., Keil, M.: The challenge of accurate software project status reporting: a two-stage model incorporating status errors and reporting bias. IEEE Transactions on Engineering Management 49, 491–504 (2002)CrossRefGoogle Scholar
  8. 8.
    Mockus, A., et al.: Two Case Studies of Open Source Software Development: Apache and Mozilla. ACM Transactions on Software Engineering and Methodology 11, 309–346 (2002)CrossRefGoogle Scholar
  9. 9.
    Wang, J.: Survival factors for Free Open Source Software projects: A multi-stage perspective. European Management Journal (2012)Google Scholar
  10. 10.
    Stewart, K.J., et al.: Impacts of license choice and organizational sponsorship on user interest and development activity in open source software projects. Information Systems Research 17, 126–144 (2006)CrossRefGoogle Scholar
  11. 11.
    Sen, R., et al.: Open source software licenses: Strong-copyleft, non-copyleft, or somewhere in between? Decision Support Systems (2011)Google Scholar
  12. 12.
    Chengalur-Smith, I., et al.: Sustainability of free/libre open source projects: A longitudinal study. Journal of the Association for Information Systems 11, 5 (2010)Google Scholar
  13. 13.
    Amrit, C., van Hillegersberg, J.: Exploring the impact of socio-technical core-periphery structures in open source software development. Journal of Information Technology 25, 216–229 (2010)CrossRefGoogle Scholar
  14. 14.
    English, R., Schweik, C.: Identifying success and abandonment of FLOSS commons: A classification of Sourceforge. net projects. Upgrade: The European Journal for the Informatics Professional VIII 6 (2007)Google Scholar
  15. 15.
    Wiggins, A., Crowston, K.: Reclassifying success and tragedy in FLOSS projects. In: Ågerfalk, P., Boldyreff, C., González-Barahona, J.M., Madey, G.R., Noll, J. (eds.) OSS 2010. IFIP AICT, vol. 319, pp. 294–307. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Sharda, R., Delen, D.: Predicting box-office success of motion pictures with neural networks. Expert Systems with Applications 30, 243–254 (2006)CrossRefGoogle Scholar
  17. 17.
    Wang, J., et al.: Human agency, social networks, and FOSS project success. Journal of Business Research (2011)Google Scholar
  18. 18.
    Howison, J., et al.: FLOSSmole: A collaborative repository for FLOSS research data and analyses. International Journal of Information Technology and Web Engineering (IJITWE) 1, 17–26 (2006)CrossRefGoogle Scholar
  19. 19.
    Kass, G.V.: An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 119–127 (1980)Google Scholar
  20. 20.
    Breiman, L., et al.: Classification and regression trees. Chapman & Hall/CRC (1984)Google Scholar
  21. 21.
    Haughton, D., Oulabi, S.: Direct marketing modeling with CART and CHAID. Journal of Interactive Marketing 11, 42–52 (1997)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • James Piggot
    • 1
  • Chintan Amrit
    • 1
  1. 1.Department of IEBISUniversity of TwenteThe Netherlands

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