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A Model for Software Development Effort and Cost Estimation
August 1997 (vol. 23 no. 8)
pp. 485-497

Abstract—Several algorithmic models have been proposed to estimate software costs and other management parameters. Early prediction of completion time is absolutely essential for proper advance planning and aversion of the possible ruin of a project. Putnam's SLIM model offers a fairly reliable method that is used extensively to predict project completion times and manpower requirements as the project evolves. However, the nature of the Norden/Rayleigh curve used by Putnam, renders it unreliable during the initial phases of the project, especially in projects involving a fast manpower buildup, as is the case with most software projects. In this paper, we propose the use of a model that improves early prediction considerably over the Putnam model. An analytic proof of the model's improved performance is also demonstrated on simulated data.

[1] B. Boehm, Software Engineering Economics, Prentice Hall, Upper Saddle River, N.J., 1981, pp. 533-535.
[2] P.V. Norden, "Curve Fitting for a Model of Applied Research and Development Scheduling," IBM J. Research and Development, vol. 3, no. 2, pp. 232-248, July 1958.
[3] F.N. Parr, "An Alternative to the Rayleigh Curve Model for Software Development Effort," IEEE Trans. Software Eng., pp. 291-296, May 1980.
[4] V.R. Basili, "Resource Models," Tutorial on Models and Metrics for Software Management and Eng., vol. IEEECatalog No. EHO 167-7, pp. 4-9, Sept. 1980.
[5] L.H. Putnam, "A General Empirical Solution to the Macro Software Sizing and Estimation Problem," IEEE Trans. Software Eng., pp. 345-361, July 1978.
[6] R.D.H. Warburton, "Managing and Predicting the Costs of Real-Time Software," IEEE Trans. Software Eng., vol. 9, no. 5, pp. 562-569, Sept. 1983.
[7] P.V. Norden, Project Life Cycle Modeling: Background and Application of the Life Cycle Curves. U.S. Army Computer Systems Command, 1977.
[8] K. Pillai and V.S.S. Nair, "Statistical Analysis of Nonstationary Software Metrics," J. Information and Software Technology, vol. 39, no. 5, pp. 363-373, 1997.
[9] W.N. Venables and B.D. Ripley, Modern Applied Statistics with S-Plus.New York: Springer-Verlag, 1994.
[10] V.R. Basili and M.V. Zelkowitz, "Analyzing Medium-Scale Software Development," Proc. Third Int'l Conf. Software Eng., IEEE, pp. 116-123, 1978.
[11] G. Casella and R.L. Berger, Statistical Inference.Belmont Calif.: Duxbury Press, 1990.
[12] F.P. Brooks, Jr., The Mythical Man-Month: Essays on Software Engineering, Addison Wesley Longman, Reading, Mass., 1975.
[13] W.D. Cooper, Electronic Instrumentation and Measurement Techniques.Englewood Cliffs, N.J.: Prentice Hall, 1970.
[14] N.F. Schneidewind, "Validating Metrics for Ensuring Space Shuttle Flight Software Quality, Computer, vol. 27, no. 8, pp. 50-57, Aug. 1994.
[15] S.D. Thompson and J.W. Thompson, Commentaries on the Law of Corporations, vol. 3, ch. 66, pp. 406-482.Indianapolis: Bobbs-Merrill, 1927.
[16] T.K. Abdel-Hamid and S.E. Madnik, "The Dynamics of Software Project Scheduling," Comm. ACM, vol. 26, pp. 340-346, May 1983.
[17] G.M. Weinberg, Quality Software Management: Systems Thinking, Vol. 1, Dorset House, New York, 1991.

Index Terms:
Completion time, development time, early prediction, Gamma model, manpower, Norden/Rayleigh model.
Krishnakumar Pillai, V.S. Sukumaran Nair, "A Model for Software Development Effort and Cost Estimation," IEEE Transactions on Software Engineering, vol. 23, no. 8, pp. 485-497, Aug. 1997, doi:10.1109/32.624305
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