The RISCOSS Platform for Risk Management in Open Source Software Adoption

  • X. Franch
  • R. Kenett
  • F. Mancinelli
  • A. Susi
  • D. Ameller
  • M. C. Annosi
  • R. Ben-Jacob
  • Y. Blumenfeld
  • O. H. Franco
  • D. Gross
  • L. Lopez
  • M. Morandini
  • M. Oriol
  • A. Siena
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 451)

Abstract

Managing risks related to OSS adoption is a must for organizations that need to smoothly integrate OSS-related practices in their development processes. Adequate tool support may pave the road to effective risk management and ensure the sustainability of such activity. In this paper, we present the RISCOSS platform for managing risks in OSS adoption. RISCOSS builds upon a highly configurable data model that allows customization to several types of scopes. It implements two different working modes: exploration, where the impact of decisions may be assessed before making them; and continuous assessment, where risk variables (and their possible consequences on business goals) are continuously monitored and reported to decision-makers. The blackboard-oriented architecture of the platform defines several interfaces for the identified techniques, allowing new techniques to be plugged in.

Keywords

Open source projects Open source software OSS Open source adoption Risk management Software platform 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Li, J., et al.: A State-of-the-Practice Survey of Risk Management in Development with Off-the-Shelf Software Components. IEEE TSE 34(2) (2008)Google Scholar
  2. 2.
    Hauge, Ø., Cruzes, D.S., Conradi, R., Velle, K.S., Skarpenes, T.A.: Risks and risk mitigation in open source software adoption: bridging the gap between literature and practice. In: Ågerfalk, P., Boldyreff, C., González-Barahona, J.M., Madey, G.R., Noll, J. (eds.) OSS 2010. IFIP AICT, vol. 319, pp. 105–118. Springer, Heidelberg (2010)Google Scholar
  3. 3.
    Franch, X., et al.: Managing risk in open source software adoption. In: ICSOFT 2013 (2013)Google Scholar
  4. 4.
    Gousios, G.: The GHTorent dataset and tool suite. In: MSR 2013 (2013)Google Scholar
  5. 5.
    Peters, F., Menzies, T., Marcus, A.: Better cross company defect prediction. In: MSR 2013 (2013)Google Scholar
  6. 6.
    D’Ambros, M., Lanza, M., Robbes, R.: An extensive comparison of bug prediction approaches. In: MSR 2010 (2010)Google Scholar
  7. 7.
    Zhang, F., Mockus, A., Keivanloo, I., Zou, Y.: Towards building a universal defect prediction model. In: MSR 2014 (2014)Google Scholar
  8. 8.
    Gamalielsson, J., Lundell, B., Lings, B.: The nagios community: an extended quantitative analysis. In: Ågerfalk, P., Boldyreff, C., González-Barahona, J.M., Madey, G.R., Noll, J. (eds.) OSS 2010. IFIP AICT, vol. 319, pp. 85–96. Springer, Heidelberg (2010)Google Scholar
  9. 9.
    Lavazza, L., Morasca, S., Taibi, D., Tosi, D.: Predicting OSS trustworthiness on the basis of elementary code assessment. In: ESEM 2010 (2010)Google Scholar
  10. 10.
    Noll, J., Seichter, D., Beecham, S.: A qualitative method for mining open source software repositories. In: Hammouda, I., Lundell, B., Mikkonen, T., Scacchi, W. (eds.) OSS 2012. IFIP AICT, vol. 378, pp. 256–261. Springer, Heidelberg (2012)Google Scholar
  11. 11.
    Piggot, J., Amrit, C.: How healthy is my project? open source project attributes as indicators of success. In: Petrinja, E., Succi, G., El Ioini, N., Sillitti, A. (eds.) OSS 2013. IFIP AICT, vol. 404, pp. 30–44. Springer, Heidelberg (2013)Google Scholar
  12. 12.
    Petrinja, E., Sillitti, A., Succi, G.: Comparing OpenBRR, QSOS, and OMM assessment models. In: Ågerfalk, P., Boldyreff, C., González-Barahona, J.M., Madey, G.R., Noll, J. (eds.) OSS 2010. IFIP AICT, vol. 319, pp. 224–238. Springer, Heidelberg (2010)Google Scholar
  13. 13.
    Ayala, C., Cruzes, D.S., Nguyen, A.D., Conradi, R., Franch, X., Höst, M., Babar, M.A.: OSS integration issues and community support: an integrator perspective. In: Hammouda, I., Lundell, B., Mikkonen, T., Scacchi, W. (eds.) OSS 2012. IFIP AICT, vol. 378, pp. 129–143. Springer, Heidelberg (2012)Google Scholar
  14. 14.
    Ligaarden, O.S., Refsdal, A., Stolen, K.: ValidKI: a method for designing key indicators to monitor the fulfillment of business objectives. In: BUSTECH 2011 (2011)Google Scholar
  15. 15.
    Yu, E.S.K.: Modelling Strategic Relationships for Process Reengineering. PhD thesis, University of Toronto, Canada (1995)Google Scholar
  16. 16.
    Nilsson, N.J.: Problem-solving Methods in Artificial Intelligence. McGraw-Hill (1971)Google Scholar
  17. 17.
    Leone, N., et al.: The DLV System for Knowledge Representation and Reasoning. ACM Transactions on Computer Logic 7(3) (2006)Google Scholar
  18. 18.
    Kenett, R.S., Zacks, S.: Modern Industrial Statistics: with applications in R, MINITAB and JMP, 2nd (edn.). John Wiley and Sons (2014). With contributions by D. AmbertiGoogle Scholar
  19. 19.
    Salter-Townshend, M., White, A., Gollini, I., Murphy, T.B.: Review of Statistical Network Analysis: Models, Algorithms, and Software. Stat. Analysis and Data Mining 5(4) (2012)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • X. Franch
    • 1
  • R. Kenett
    • 2
  • F. Mancinelli
    • 3
  • A. Susi
    • 4
  • D. Ameller
    • 1
  • M. C. Annosi
    • 5
  • R. Ben-Jacob
    • 2
  • Y. Blumenfeld
    • 2
  • O. H. Franco
    • 1
  • D. Gross
    • 4
  • L. Lopez
    • 1
  • M. Morandini
    • 4
  • M. Oriol
    • 1
  • A. Siena
    • 4
  1. 1.Universitat Politècnica de Catalunya (UPC)BarcelonaSpain
  2. 2.KPARaananaIsrael
  3. 3.XWikiParisFrance
  4. 4.Fondazione Bruno Kessler (FBK)TrentoItaly
  5. 5.TEI - EricssonRomeItaly

Personalised recommendations