Project Cost Overrun Simulation in Software Product Line Development

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4589)


The cost of a Software Product Line (SPL) development project sometimes exceeds the initially planned cost, because of requirements volatility and poor quality. In this paper, we propose a cost overrun simulation model for time-boxed SPL development. The model is an enhancement of a previous model, specifically now including: consideration of requirements volatility, consideration of unplanned work for defect correction during product projects, and nominal project cost overrun estimation. The model has been validated through stochastic simulations with fictional SPL project data, by comparing generated unplanned work effort to actual change effort, and by sensitivity analysis. The result shows that the proposed model has reasonable validity to estimate nominal project cost overruns and its variability. Analysis indicates that poor management of requirements and quality will almost double estimation error, for the studied simulation settings.


process simulation cost overrun estimation software product line development 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Clements, P., Northrop, L.M.: Software Product Lines: Practices and Patterns. Addison-Wesley, MA (2001)Google Scholar
  2. 2.
    Schmid, K., Biffl, S.: Systematic management of software product lines. Softw. Process Improve. Pract. 10, 61–76 (2005)CrossRefGoogle Scholar
  3. 3.
    Genuchten, M.v.: Why is software late? an empirical study of reasons for delay in software development. IEEE Trans. Softw. Eng. 17 (1991)Google Scholar
  4. 4.
    Subramanian, G.H., Breslawski, S.: An empirical analysis of software effort estimate alterations. J. Systems and Software 31, 135–141 (1995)CrossRefGoogle Scholar
  5. 5.
    Nurmuliani, N., Zowghi, D., Fowell, S.: Analysis of requirements volatility during software development life cycle. In: Proc. 2004 Australian Softw. Eng. Conf. (ASWEC 2004) (2004)Google Scholar
  6. 6.
    Dijkstra, E.: Notes on structured programming. In: Dahl, O.J., Dijkstra, E., Hoare, C.A.R. (eds.) Structured Programming, Academic Press, London (1972)Google Scholar
  7. 7.
    Nonaka, M., Zhu, L., Babar, M.A., Staples, M.: Project delay variability simulation in software product line development. In: Proc. Intl. Conf. Software Process (ISCP) (to appear)Google Scholar
  8. 8.
    IEEE: Ieee std. 1219-1998, ieee standard for software maintenance (1998)Google Scholar
  9. 9.
    Jørgensen, M., Moløkken, K.: Reasons for software effort estimation error: Impact of respondent role. information collection approach, and data analysis method. IEEE Trans. Softw. Eng. 30, 993–1007 (2004)Google Scholar
  10. 10.
    Jørgensen, M.: Realism in assessment of effort estimation uncertainty: It matters how you ask. IEEE Trans. Softw. Eng. 2004, 209–217 (2004)CrossRefGoogle Scholar
  11. 11.
    Procaccino, J.D., Verner, J.M.: Software project managers and project success: An exploratory study. J. Systems and Software 79, 1541–1551 (2006)CrossRefGoogle Scholar
  12. 12.
    Boehm, B.W., Abts, C., Brown, A.W., Chulani, S., Clark, B.K., Horowitz, E., Madachy, R., Reifer, D., Steece, B.: Software Cost Estimation with COCOMO II. Prentice-Hall, Englewood Cliffs (2000)Google Scholar
  13. 13.
    Boehm, B.W., Brown, A.W., Madachy, R., Yang, Y.: A software product line life cycle cost estimation model. In: Proc, Intl. Symp. Empirical Softw. Eng. (ISESE 2004), pp. 156–164 (2004)Google Scholar
  14. 14.
    Chen, Y., Gannod, G.C., Collofello, J.S.: A software product line process simulator. Softw. Process Improve. Pract. 11, 385–409 (2006)CrossRefGoogle Scholar
  15. 15.
    Yang, D., Wan, Y., Tang, Z., Wu, S., He, M., Li, M.: Cocomo-u: An extension of cocomo ii for cost estimation with uncertainty. In: Wang, Q., Pfahl, D., Raffo, D.M., Wernick, P. (eds.) Software Process Change. LNCS, vol. 3966, pp. 132–141. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Bosch, J.: Maturity and evolution in software product lines: Approaches, artefacts and organization. In: Proc. 2nd Intl. Conf. Softw. Product Lines 2002, pp. 257–271 (2002)Google Scholar
  17. 17.
    Epping, A., Lott, C.M.: Does software design complexity affect maintenance effort? In: Proc. 19th Softw. Eng. Workshop. 1994, pp. 297–313 (1994)Google Scholar
  18. 18.
    Bocco, M.G., Moody, D.L., Piattini, M.: Assessing the capability of internal metrics as early indicators of maintenance effort through experimentation. J. Software Maintenance and Evolution 17, 225–246 (2005)CrossRefGoogle Scholar
  19. 19.
    Ramanujan, S., Scamell, R.W., Shah, J.R.: An experimental investigation of the impact of individual, program, and organizational characteristics on software maintenance effort. J. Systems and Software 54, 137–157 (2000)CrossRefGoogle Scholar
  20. 20.
    Kan, S.H., Dull, S.D., Amundson, D.N., Lindner, R.J., Hedger, R.J.: As/400 software quality management. IBM Systems Journal 33, 62–88 (1994)CrossRefGoogle Scholar
  21. 21.
    Remus, H.: Integrated software validation in the view of inspections / reviews. In: Proc. Symposium on Softw. Validation, pp. 57–64. Elsevier, North-Holland (1983)Google Scholar
  22. 22.
    SEL: Sel, (software engineering laboratory) data (1997),
  23. 23.
    Musa, J.D.: Software Reliability Engineering. Osborne/McGraw-Hill (1998)Google Scholar
  24. 24.
    Defamie, M., Jacobs, P., Thollembeck, J.: Software reliability: assumptions, realities and data. In: Proc. 1999 Intl. Conf. Softw. Maintenance (ICSM 1999), pp. 337–345 (1999)Google Scholar
  25. 25.
    Miyazaki, Y., Takanou, A., Nozaki, H., Nakagawa, N., Okada, K.: Method to estimate parameter values in software prediction models. Inf. Softw. Technol. 33, 239–243 (1991)CrossRefGoogle Scholar
  26. 26.
    Moløkken, K., Jørgensen, M.: A comparison of software project overruns–flexible versus sequential development models. IEEE Trans. Softw. Eng. 31, 754–766 (2005)CrossRefGoogle Scholar
  27. 27.
    Sargent, R.G.: Validation and verification of simulation models. In: Proc. 31st Conf. Winter Simulation, pp. 39–48 (1999)Google Scholar
  28. 28.
    Donzelli, P.: A decision support system for software project management. IEEE Software 23, 67–75 (2006)CrossRefGoogle Scholar
  29. 29.
    Abdel-Hamid, T., Madnick, S.: Software Project Dynamics- An Integrated Approach. Prentice-Hall, Englewood Cliffs, NJ (1991)Google Scholar
  30. 30.
    Calavaro, G.F., Basili, V.R., Iazeolla, G.: Simulation modeling of software development process. In: Proc. 7th European Simulation Symposium. Soc. for Computer Simulation (1995)Google Scholar
  31. 31.
    Antoniol, G., Cimitile, A., Lucca, G.A., Penta, M.: Assessing staffing needs for a software maintenance project through queuing simulation. IEEE Trans. Softw. Eng. 30, 43–58 (2004)CrossRefGoogle Scholar
  32. 32.
    Martin, R., Raffo, D.: Application of a hybrid process simulation model to a software development project. J. Systems and Software 59, 237–246 (2001)CrossRefGoogle Scholar
  33. 33.
    Kellner, M.I., Madachy, R.J., Raffo, D.M.: Software process simulation modeling: Why? what? how? J. Systems and Software 46, 113–122 (1999)CrossRefGoogle Scholar
  34. 34.
    Cohen, S.: Predicting when product line investment pays. Technical Report Techinical Report CMU/SEI-2003-TN-017, Software Engineering Institute, Carnegie Mellon University (2003)Google Scholar
  35. 35.
    Böckle, G., Clements, P., McGregor, J.D., Muthig, D., Schmid, K.: Calculating roi for software product lines. IEEE Software 21, 32–38 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  1. 1.Faculty of Business Administration, Toyo UniversityJapan
  2. 2.National ICTAustralia
  3. 3.Lero, University of LimerickIreland

Personalised recommendations