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T.M. Khoshgoftaar, J.C. Munson, B.B. Bhattacharya, G.D. Richardson, "Predictive Modeling Techniques of Software Quality from Software Measures," IEEE Transactions on Software Engineering, vol. 18, no. 11, pp. 979987, November, 1992.  
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@article{ 10.1109/32.177367, author = {T.M. Khoshgoftaar and J.C. Munson and B.B. Bhattacharya and G.D. Richardson}, title = {Predictive Modeling Techniques of Software Quality from Software Measures}, journal ={IEEE Transactions on Software Engineering}, volume = {18}, number = {11}, issn = {00985589}, year = {1992}, pages = {979987}, doi = {http://doi.ieeecomputersociety.org/10.1109/32.177367}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Software Engineering TI  Predictive Modeling Techniques of Software Quality from Software Measures IS  11 SN  00985589 SP979 EP987 EPD  979987 A1  T.M. Khoshgoftaar, A1  J.C. Munson, A1  B.B. Bhattacharya, A1  G.D. Richardson, PY  1992 KW  predictive modelling process; software quality; software measures; software development life cycle; software system; software complexity; regression modeling; least squares; least absolute value estimation; predictive quality; software metrics; software quality; statistical analysis VL  18 JA  IEEE Transactions on Software Engineering ER   
The objective in the construction of models of software quality is to use measures that may be obtained relatively early in the software development life cycle to provide reasonable initial estimates of the quality of an evolving software system. Measures of software quality and software complexity to be used in this modeling process exhibit systematic departures of the normality assumptions of regression modeling. Two new estimation procedures are introduced, and their performances in the modeling of software quality from software complexity in terms of the predictive quality and the quality of fit are compared with those of the more traditional least squares and least absolute value estimation techniques. The two new estimation techniques did produce regression models with better quality of fit and predictive quality when applied to data obtained from two software development projects.
[1] I. Barrodale, "L1approximation and the analysis of data,"Applied Statistics, vol. 17, pp. 5156, 1968.
[2] G. Bassett and R. Koenker, "Asymptotic theory of least absolute error regressions,"J. Am. Statistical Association, vol. 73, pp. 618622, 1978.
[3] P. Bloomfield and W. L. Steiger,Least Absolute Deviations: Theory, Applications, and Algorithms. Boston, MA: Birkhauser, 1983.
[4] T. Dielman, "A comparison of forecasts from least absolute value and least squares regression,"J. Forecasting, vol. 5, pp. 189195, 1986.
[5] Y. Dodge, "An introduction to L1norm based, statistical data analysis,"J. Computational Statistics&Data Analysis, vol. 5, pp. 239253, 1987.
[6] M. Halstead,Elements of Software Science. New York: ElsevierNorth Holland, 1977.
[7] D. Ince and M.J. Shepperd, "System design metrics: a review and perspective," inProc. IEE/BCS Conf. Software Engineering, pp. 2327, 1988.
[8] B. Kitchenham and L. Pickard, "Toward a constructive quality modelPart II: Statistical techniques for modeling software quality in the ESPRIT REQUEST project,"Software Engineering J., vol. 2, no. 4, pp. 114126, July 1987.
[9] A. Levitin, "The L1criteria in data analysis and the problem of software size estimation," inProc. 21st Symp. Interface: Computing Science and Statistics, pp. 382384, 1989.
[10] R. K. Lind and K. Vairavan, "An experimental investigation of software metrics and their relationship to software development effort,"IEEE Trans. Software Eng., vol. 15, pp. 649651, May 1989.
[11] T. McCabe, "A complexity measure,"IEEE Trans. Soft. Eng., vol. 2, pp. 308320, 1976.
[12] J. C. Munson and T. M. Khoshgoftaar, "Regression modeling of software quality: an empirical investigation,"Inform. Software Tech., vol. 32, no. 2, pp. 106114, Mar. 1990.
[13] R.H. Myers,Classical and Modern Regression with Applications. Boston, MA: Duxbury Press, 1990.
[14] S. Narula and J. Wellington, "Prediction, linear regression, and minimum sum of relative errors,"Tecknometrics, vol. 19, pp. 185190, May 1977.
[15] L. Pickard, "Analysis of software metrics," in B. Kitchenham and B. Littlewood, Eds.Measurement for Software Control Assurance. New York: Elsevier Applied Science, pp. 155180, 1989.
[16] A. A. Porter and R. W. Selby, "Empirically guided software development using metricbased classification trees,"IEEE Software, vol. 7, no. 2, pp. 4654, Mar. 1990.
[17] J. Rice and J. White, "Norms for smoothing and estimation,"SIAM Review, vol. 6, pp. 243256, 1964.
[18] V. Y. Shen, T. J. Yu, S. M. Thebaut, and L. R. Paulsen, "Identifying errorprone softwareAn empirical study,"IEEE Trans. Software Eng., vol. SE11, no. 4, pp. 317324, Apr. 1985.
[19] M. Shepperd and D. Ince, "Metrics, outlier analysis and the software design process,"Inf. Soft. Tech., vol. 31, pp. 9198, Mar. 1989.
[20] S. Stigler, "Gauss and the invention of least squares,"Annals of Statistics, vol. 9, pp. 465474, 1981.
[21] D. Weiss and V. Basili, "Software development by analysis of changes: some data from the Software Engineering Laboratory,"IEEE Trans. Soft. Eng., vol. 11, pp. 157168, Feb. 1985.
[22] H. Wilson, "Leastsquares versus minimum absolute deviations estimation in linear models,"Decision Sciences, vol. 9, pp. 322335, 1978.