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Experience With the Accuracy of Software Maintenance Task Effort Prediction Models
August 1995 (vol. 21 no. 8)
pp. 674-681
This paper reports experience from the development and use of eleven different software maintenance effort prediction models. The models were developed applying regression analysis, neural networks and pattern recognition and the prediction accuracy was measured and compared for each model type. The most accurate predictions were achieved applying models based on multiple regression and on pattern recognition. We suggest the use of prediction models as instruments to support the expert estimates and to analyse the impact of the maintenance variables on the maintenance process and product. We believe that the pattern recognition based models evaluated, i.e., the prediction models based on the Optimized Set Reduction method, show potential for such use.

[1] R.D. Banker, S.M. Datar, C.F. Kemerer, and D. Zweig, "Software Complexity and Maintenance Costs," Comm. ACM, vol. 36, pp. 81-94, Nov. 1993.
[2] L.C. Briand and V.R. Basili, A Classification Procedure for an Effective Management of Changes during the Software Maintenance Process Proc. IEEE Int'l Conf. Software Maintenance, 1992.
[3] W.A. Delaney,“Predicting the costs of computer programs,” Data Processsing Magazine, pp. 32-34, 1966.
[4] C.R. Symons, Software Sizing and Estimating MKII FPA. John Wiley and Sons, 1991.
[5] B.P. Lientz and E.B. Swanson, Software Maintenance Management: A Study of the Maintenance of Computer Application Software in 487 Data Processing Organizations, Addison Wesley Longman, Reading, Mass., 1980.
[6] D.M. Balda,“Cost estimation models for the reuse and prototype software development life-cycles,” ACM Software Eng. Notes, vol.15, pp. 42-50, July 1990.
[7] G.C. Low and R.D. Jeffery,“Function points in the estimation and evaluation of the software process,” IEEE Transactions on Software Engineering, vol. 16, pp. 64-71, Jan. 1990.
[8] A.S. Wang and H.E. Dunsmore,“Early software size estimation: A critical analysis of the software science length equation and adata-structure-oriented size estimation approach,” Proc. Third Symp. on Empirical Foundations of Information and Software Sciences, Oct. 1985.
[9] T.H. Wonnacott and R.J. Wonnacott,Introductory Statistics, 5th ed. New York: John Wiley and Sons, 1990.
[10] G.B. Wetherill,Intermediate Statistical Methods.New York: Chapman and Hall, 1981.
[11] G.S. Maddala,Econometrics.New York: McGraw-Hill, 1977.
[12] J. Hertz, A. Krogh, and R.G. Palmer, Introduction to the Theory of Neural Computation. Addison-Wesley, 1991.
[13] L.C. Briand, V.R. Basili, and W.M. Thomas, "A Pattern Recognition Approach for Software Engineering Data Analysis," IEEE Trans. Software Eng., vol. 18, no. 11, pp. 931-942, 1992.
[14] L.C. Briand,V.R. Basili,, and W.M. Thomas,“Recognizing patterns for software development prediction and evaluation,” T.R Gulledge and W.P. Hutzler, eds., Analytical Methods in Software Engineering Economics, pp. 151-170.Heidelberg: Springer-Verlag, 1993.
[15] L.C. Briand,V.R. Basili,, and C.J. Hetmanski,“Developing interpretable models with optimized set reduction for identifying high-risk software components,” IEEE Transactions on Software Engineering, vol. 19, no. 11, pp. 1,028-1,044, Nov. 1993.
[16] B. Boehm, Software Engineering Economics, Prentice Hall, Upper Saddle River, N.J., 1981, pp. 533-535.
[17] B.A. Kitchenham, "Empirical Studies of Assumptions that Underlie Software Cost-Estimation Models," Information and Software Technology, vol. 34, no. 4, pp. 211-218, 1992.
[18] D.A. Ratkowsky,Nonlinear Regression Modeling.New York: Marcel Dekker, 1983.
[19] Y. Miyata,A User’s Guide to PlaNet Version 5.6.Boulder: University of Colorado, 1991.
[20] E. Bradley and G. Gong,“A leisurely look at the bootstrap, the jackknife, and cross-validation,” Amer. Statistician, vol. 37, no. 1, pp. 36-48, 1983.
[21] S.D. Conte, H. E. Dunsmore, and V. Y. Shen, Software Engineering Metrics and Models, Benjamin/Cummings, Menlo Park, Calif., 1986.
[22] T. Abdel-Hamid and S. Madnic,“Impact of schedule estimation on software project behavior,” IEEE Software, vol. 3, no. 4, pp. 70-75, July 1986.
[23] R.D. Banker and C.F. Kemerer, "Scale of Economies in New Software Development," IEEE Trans. Software Eng., vol. 15, no. 10, pp. 1,199-1,205, 1989.

Index Terms:
Cost models, neural network, pattern recognition, prediction models, regression, software maintenance, software measurement.
Citation:
Magne Jørgensen, "Experience With the Accuracy of Software Maintenance Task Effort Prediction Models," IEEE Transactions on Software Engineering, vol. 21, no. 8, pp. 674-681, Aug. 1995, doi:10.1109/32.403791
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