Advertisement

A Methodology for Assessing Schedule Risk in Software Development Estimates

Conference paper
  • 86 Downloads

Abstract

Planning estimates for software development schedules are often obtained using a schedule-estimating equation that expresses the schedule estimate (in calendar months) as a nonlinear function of the total development effort (in staff-months). For example, the COCOMO schedule model [1] for embedded-mode developments is Schedule = 2.5 (Effort)0.32 We address the problem of measuring the error of uncertainty in the schedule estimate when the error of the effort estimate and the error of the schedule model (given actual effort data) are known.

We illustrate the methodology for two models calibrated to historical cost data.

Keywords

Software Development Beta Distribution Effort Estimate Triangular Distribution Nonzero Probability 
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., Software Engineering Economics, Prentice-Hall, Inc., Englewood Cliffs, NJ, 1981.zbMATHGoogle Scholar
  2. 2.
    Funch, P. G., “Recalibration of Basic and Nominal COCOMO Equations to Recent Air Force Acquisitions,” presented at the Third Annual COCOMO Users’ Group Meeting, Pittsburgh, PA, 3–5 November 1987.Google Scholar
  3. 3.
    Wolfinger, B. E., “Calibrating COCOMO for Ada,” presented at the Third Annual COCOMO Users’ Group Meeting, Pittsburgh, PA, 3–5 November 1987.Google Scholar
  4. 4.
    Pullen, K. W., “Uncertainty Analysis with COCOMO,” presented at the Third Annual COCOMO Users’ Group Meeting, Pittsburgh, PA, 3–5 November 1987.Google Scholar
  5. 5.
    Keefer, D.L., S. E. Bodily, “Three-Point Approximations for Continuous Random Variables,” Management Science, Vol. 29, pp. 595–609 (1983).zbMATHCrossRefGoogle Scholar
  6. 6.
    “Risk and Uncertainty,” Section IV in Cost Analysis, G. R. McNichols, ed., selected papers from the symposium, “Operations Research in Cost Analysis,” Arlington, VA, 18–20 May 1983; publ. by the Operations Research Society of America, 1984.Google Scholar
  7. 7.
    Garvey, P. R., F. D. Powell, “Three Methods for Quantifying Software Development Effort Uncertainty,” pp. 292–306 in Software Risk Management, B. W. Boehm, ed., an IEEE tutorial, publ. by the IEEE Computer Society Press, Washington, DC, 1989.Google Scholar
  8. 8.
    Bratley, P, B. L. Fox, L. E. Schrage, A Guide to Simulation, 2nd ed., Springer-Verlag, New York, NY, Berlin, 1987.CrossRefGoogle Scholar
  9. 9.
    Lilliefors, H. W., “On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown,” Journal of the American Statistical Association, Vol. 62, pp. 399–402 (1967).CrossRefGoogle Scholar
  10. 10.
    Liffiefors, H. W, “On the Kolmogorov-Smirnov Test for the Exponential Distribution with Mean Unknown,” Journal of the American Statistical Association, Vol. 64, pp. 387–389 (1969).CrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York Inc. 1991

Authors and Affiliations

  1. 1.The MITRE CorporationBedfordUSA

Personalised recommendations