Download Patterns and Releases in Open Source Software Projects: A Perfect Symbiosis?

Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 319)


Software usage by end-users is one of the factors used to evaluate the success of software projects. In the context of open source software, there is no single and non-controversial measure of usage, though. Still, one of the most used and readily available measure is data about projects downloads. Nevertheless, download counts and averages do not convey as much information as the patterns in the original downloads time series. In this research, we propose a method to increase the expressiveness of mere download rates by considering download patterns against software releases. We apply experimentally our method to the most downloaded projects of SourceForge’s history crawled through the FLOSSMole repository. Findings show that projects with similar usage can have indeed different levels of sensitivity to releases, revealing different behaviors of users. Future research will develop further the pattern recognition approach to automatically categorize open source projects according to their download patterns.


Open source software projects software releases repository mining 


  1. 1.
    Chakrabarti, K., Keogh, E., Mehrotra, S., Pazzani, M.: Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. 27(2), 188–228 (2002)CrossRefGoogle Scholar
  2. 2.
    Crowston, K., Annabi, H., Howison, J.: Defining Open Source Software Project Success. In: Crowston, K., Annabi, H., Howison, J. (eds.) Proceedings of the 24th International Conference on Information Systems (ICIS), pp. 327–340 (2003)Google Scholar
  3. 3.
    Crowston, K., Annabi, H., Howison, J., Masango, C.: Towards a portfolio of FLOSS project success measures. In: The 4th workshop on Open Source Software engineering, International Conference on Software Engineering (2004)Google Scholar
  4. 4.
    Delone, W.H., McLean, E.R.: The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. J. Management of Information Systems 19, 9–30 (2003)Google Scholar
  5. 5.
    Howison, J., Conklin, M., Crowston, K.: FLOSSmole: A collaborative repository for FLOSS research data and analyses. International Journal of Information Technology and Web Engineering 1(3), 17–26 (2006)Google Scholar
  6. 6.
    Israeli, A., Feitelson, D.G.: Success of Open Source Projects: Patterns of Downloads and Releases with Time. In: IEEE International Conference Software Science, Technology, & Engineering, pp. 87–94 (2007)Google Scholar
  7. 7.
    Feitelson, D.G., Heller, G.Z., Schach, S.R.: An Empirically-Based Criterion for Determining the Success of an Open-Source Project. In: Proceedings of Australian Software Engineering Conference, pp. 363–368 (2006)Google Scholar
  8. 8.
    Li, T., Li, Q., Zhu, S., Ogihara, M.: A Survey on Wavelet Applications in Data Mining. SIGKDD Explor. Newsl. 4(2), 49–68 (2002)CrossRefGoogle Scholar
  9. 9.
    Rossi, B., Russo, B., Succi, G.: Analysis of Open Source Software Development Iterations by means of Burst Detection Techniques. In: Proceedings of the 5th International Conference on Open Source Systems, pp. 83–93. Springer, Boston (2009)Google Scholar
  10. 10.
    Wiggins, A., Howison, J., Crowston, K.: Measuring Potential User Interest and Active User Base in FLOSS Projects. In: proceedings of the 5th International Conference on Open Source Systems, pp. 94–104 (2009)Google Scholar
  11. 11.
    Weiss, D.: Measuring Success of Open Source Projects using Web Search Engines. In: Scotto, M., Giancarlo, S. (eds.) Proceedings of the 1st International Conference on Open Source Systems, Genova, Italy, pp. 93–99 (2005)Google Scholar

Copyright information

© IFIP 2010

Authors and Affiliations

  1. 1.CASE – Center for Applied Software EngineeringFree University of Bolzano-BozenBolzanoItaly

Personalised recommendations