This Article 
 Bibliographic References 
 Add to: 
Better Reliability Assessment and Prediction through Data Clustering
October 2002 (vol. 28 no. 10)
pp. 997-1007

Abstract—This paper presents a new approach to software reliability modeling by grouping data into clusters of homogeneous failure intensities. This series of data clusters associated with different time segments can be directly used as a piecewise linear model for reliability assessment and problem identification, which can produce meaningful results early in the testing process. The dual model fits traditional software reliability growth models (SRGMs) to these grouped data to provide long-term reliability assessments and predictions. These models were evaluated in the testing of two large software systems from IBM. Compared with existing SRGMs fitted to raw data, our models are generally more stable over time and produce more consistent and accurate reliability assessments and predictions.

[1] J.R. Brown and M. Lipow,“Testing for software reliability,” Proc. Int’l Conf. Reliable Software, pp. 518-527,Los Angeles, Calif, Apr. 1975.
[2] K.-Y. Cai, “Censored Software-Reliability Models,” IEEE Trans. Reliability, vol. 46, no. 1, pp. 69–75, Mar. 1997.
[3] L.A. Clark and D. Pregibon, “Tree Based Models,” Statistical Models in S, J.M. Chambers and T.J. Hastie, eds., chapter 9, pp. 377–419, 1993.
[4] K. Gocva-Popstojanova and K.S. Trivedi, “Failure Crrelation in Sftware Rliability Mdels,” IEEE Trans. Reliability, vol. 49, no. 1, pp. 37–48, Mar. 2000.
[5] A.L. Goel, “Software Reliability Models: Assumptions, Limitations, and Applicability,” IEEE Trans. Software Eng., vol. 11, no. 12, pp. 1411–1423, Dec. 1985.
[6] A.L. Goel and K. Okumoto, “A Time Dependent Error Detection Rate Model for Software Reliability and Other Performance Measures,” IEEE Trans. Reliability, vol. 28, no. 3, pp. 206–211, 1979.
[7] Z. Jelinski and P.L. Moranda, “Software Reliability Research,” Statistical Computer Performance Evaluation, W. Freiberger, ed., pp. 365–484, 1972.
[8] K. Kanoun and J.-C. Laprie, “Trend Analysis,” Handbook of Software Reliability Eng., M.R. Lyu, ed., pp. 401–437, 1995.
[9] P.A. Keiller and T.A. Mazzuchi, “Investigating a Specific Class of Software Reliability Growth Models,” Proc. Ann. Reliability and Maintainability Symp., pp. 242-248, Jan. 2002.
[10] B. Littlewood and J.L. Verrall, “A Bayesian Reliability Growth Model for Computer Software,” Applied Statistics, vol. 22, no. 3, pp. 332–346, 1973.
[11] Handbook of Software Reliability Engineering, M.R. Lyu, ed. New York: McGraw-Hill, 1995.
[12] J.D. Musa,A. Iannino,, and K. Okumoto,Software Reliability: Measurement, Prediction and Application.New York: McGraw-Hill, 1987.
[13] J.D. Musa and K. Okumoto, "A Logarithmic Poisson Execution Time Model for Software Reliability Measurement," Proc. Seventh Int'l Conf. Software Eng., IEEE CS Press, Los Alamitos, Calif., 1984, pp. 230-238.
[14] E. Nelson, “Estimating Software Reliability from Test Data,” Microelectronics and Reliability, vol. 17, no. 1, pp. 67–73, 1978.
[15] N.F. Schneidewind, “Analysis of Error Processes in Computer Software,” Proc. Int'l Conf. Reliable Software, pp. 337–346, Apr. 1975.
[16] N.F. Schneidewind, “Software Reliability Model with Optimal Selection of Failure Data,” IEEE Trans. Software Eng., vol. 19, no. 11, pp. 1,095-1,104, Nov. 1993.
[17] N. Singpurwalla, “Software Reliability Modeling by Concatenating Failure Rates,” Proc. Ninth Int'l Symp. Software Reliability Eng., pp. 106–110, Nov. 1998.
[18] J. Tian, “Integrating Time Domain and Input Domain Analyses of Software Reliability Using Tree-Based Models,” IEEE Trans. Software Eng., vol. 21, no. 12, pp. 945–958, Dec. 1995.
[19] J. Tian,P. Lu,, and J. Palma,“Test execution based reliability measurement and modeling for largecommercial software,” IEEE Trans. Software Engineering, vol. 21, no. 5, pp. 405-414, May 1995.
[20] J. Tian and J. Palma, “Test Workload Measurement and Reliability Analysis for Large Commercial Software Systems,” Annals of Software Eng., vol. 4, pp. 201–222, Aug. 1997.
[21] W.N. Venables and B.D. Ripley, “Multivariate Analysis,” Modern Applied Statistics with S-Plus, chapter 12, pp. 301–328, 1994.
[22] S. Yamada, M. Ohba, and S. Osaki, “S-Shaped Reliability Growth Modeling for Software Error Detection,” IEEE Trans. Reliability, vol. 32, no. 5, pp. 475–478, Dec. 1983.

Index Terms:
Software reliability, data grouping, cluster analysis, software reliability growth models (SRGMs), input domain reliability models (IDRMs), data cluster based reliability models (DCRMs).
Jeff Tian, "Better Reliability Assessment and Prediction through Data Clustering," IEEE Transactions on Software Engineering, vol. 28, no. 10, pp. 997-1007, Oct. 2002, doi:10.1109/TSE.2002.1041055
Usage of this product signifies your acceptance of the Terms of Use.