
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
N.F. Schneidewind, "Software Reliability Model with Optimal Selection of Failure Data," IEEE Transactions on Software Engineering, vol. 19, no. 11, pp. 10951104, November, 1993.  
BibTex  x  
@article{ 10.1109/32.256856, author = {N.F. Schneidewind}, title = {Software Reliability Model with Optimal Selection of Failure Data}, journal ={IEEE Transactions on Software Engineering}, volume = {19}, number = {11}, issn = {00985589}, year = {1993}, pages = {10951104}, doi = {http://doi.ieeecomputersociety.org/10.1109/32.256856}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Software Engineering TI  Software Reliability Model with Optimal Selection of Failure Data IS  11 SN  00985589 SP1095 EP1104 EPD  10951104 A1  N.F. Schneidewind, PY  1993 KW  software reliability model; failure data; failure counts; moving average; data aging techniques; exponential smoothing; Schneidewind nonhomogeneous Poisson process; failure count interval index; data vectors; weighted least squares; constant variance; mean square error; US Space Shuttle onboard software; NHPP software reliability; aerospace computing; maximum likelihood estimation; software reliability; space vehicles VL  19 JA  IEEE Transactions on Software Engineering ER   
The possibility of obtaining more accurate predictions of future failures by excluding or giving lower weight to the earlier failure counts is suggested. Although data aging techniques such as moving average and exponential smoothing are frequently used in other fields, such as inventory control, the author did not find use of data aging in the various models surveyed. A model that includes the concept of selecting a subset of the failure data is the Schneidewind nonhomogeneous Poisson process (NHPP) software reliability model. In order to use the concept of data aging, there must be a criterion for determining the optimal value of the starting failure count interval. Four criteria for identifying the optimal starting interval for estimating model parameters are evaluated The first two criteria treat the failure count interval index as a parameter by substituting model functions for data vectors and optimizing on functions obtained from maximum likelihood estimation techniques. The third uses weighted least squares to maintain constant variance in the presence of the decreasing failure rate assumed by the model. The fourth criterion is the familiar mean square error. It is shown that significantly improved reliability predictions can be obtained by using a subset of the failure data. The US Space Shuttle onboard software is used as an example.
[1] A. A. AbdelGhaly, P. Y. Chan, and B. Littlewood, "Evaluation of competing software reliability predictions,"IEEE Trans. Software Eng., vol. SE12, no. 9, Sept. 1986.
[2] Recommended Practice for Software Reliability, R0131992, American National Standards Institute/American Institute of Aeronautics and Astronautics, 370 L'Enfant Promenade, SW, Washington, DC 20024, 1993.
[3] R. G. Brown,Smoothing, Forecasting and Prediction of Discrete Time Series. Englewood Cliffs, NJ: PrenticeHall, 1963.
[4] C. Daniel and F. S. Wood,Fitting Equations to Data. New York: WileyInterscience, 1971.
[5] N. R. Draper and H. Smith,Applied Regression Analysis. New York: Wiley, 1966.
[6] W. H. Farr, "A survey of software reliability modeling and estimation," Naval Surface Weapons Center, Tech. Rep. NSWC TR 82171, Sept. 1983.
[7] W. H. Farr and O. D. Smith, "Statistical modeling and estimation of reliability functions for software (SMERFS) users guide," Naval Surface Weapons Center, Tech. Rep. NAVSWC TR84373, rev. 2, Mar. 1991.
[8] W. H. Farr and O. D. Smith, "Statistical modeling and estimation of reliability functions for software (SMERFS) library access guide," Naval Surface Weapons Center, Tech. Rep. NAVSWC TR84371, rev. 2, Mar. 1991.
[9] A. L. Goel, "Software reliability models: Assumptions, limitations, and applicability,"IEEE Trans. Software Eng., vol. SE11, no. 12, pp. 14111423, Dec. 1985.
[10] G. M. Jenkins and D. G. Watts,Spectral Analysis and its Applications. New York: HoldenDay, 1968.
[11] K. Kanoun, M. R. Bastos Martini, and J. Moreira de Souza, "A method for software reliability and analysis and prediction application to the TROPICOR switching system,"IEEE Trans. Software Eng., vol. 17, no. 4, pp. 334344, Apr. 1991.
[12] T. M. Khoshgoftarr, A. S. Pandya, and H. B. More, "A neural network approach for predicting software development faults," inProc. 3rd Int. Symp. on Software Reliability Engineering. New York: IEEE Computer Society Press, Oct. 1992, pp. 8389.
[13] T. M. Khoshgoftarr, J. C. Munson, B. B. Bhattacharya, and G. D. Richardson, "Predictive modeling techniques of software quality from software measures,"IEEE Trans. Software Eng., vol. 18, no. 11, pp. 979987, Nov. 1992.
[14] B. Littlewood, "Theories of software reliability: How good are they and how can they be improved,"IEEE Trans. Software Eng., vol. SE6, no. 5, pp. 489500, Sept. 1980.
[15] M. Lu, S. Brocklehurst, and B. Littlewood, "Combination of predictions obtained from different software reliability growth models," inProc. 10th Annu. Software Reliability Symp., Denver, CO, June 1992, pp. 2433.
[16] M. R. Bastos Martini, K. Kanoun, and J. Moreira de Souza, "Software reliability evaluation of the TROPICOR switching system,"IEEE Trans. Rel., vol. 39, no. 3, pp. 369379, Aug. 1990.
[17] N. F. Schneidewind, "Analysis of error processes in computer software," inProc. Int. Conf. Reliable Software, Apr. 2123, 1975, pp. 337346.
[18] N. F. Schneidewind, "Analysis of error processes in computer software," inProc. Int. Conf. Reliable Software, Apr. 2123, 1975, pp. 337346.
[19] N. F. Schneidewind, "Methodology for validating software metrics,"IEEE Trans. Software Eng., vol. 18, no. 5, pp. 410422, May 1992.
[20] N. F. Schneidewind and T. W. Keller, "Application of reliability models to the Space Shuttle,"IEEE Software, pp. 2833, July 1992.
[21] M. Xie and M. Zhao, "The Schneidewind software reliability model revisited," inProc. Int. Symp. Software Reliability Engineering. New York: IEEE Computer Society Press, Oct. 1992, pp. 184192.