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Prediction of Software Reliability Using Connectionist Models
July 1992 (vol. 18 no. 7)
pp. 563-574

The usefulness of connectionist models for software reliability growth prediction is illustrated. The applicability of the connectionist approach is explored using various network models, training regimes, and data representation methods. An empirical comparison is made between this approach and five well-known software reliability growth models using actual data sets from several different software projects. The results presented suggest that connectionist models may adapt well across different data sets and exhibit a better predictive accuracy. The analysis shows that the connectionist approach is capable of developing models of varying complexity.

[1] A. A. Abdel-Ghaly, P. Y. Chan, and B. Littlewood, "Evaluation of competing software reliability predictions,"IEEE Trans. Software Eng., vol. SE-12, no. 9, Sept. 1986.
[2] B. M. Anna-Mary, "A study of the Musa reliability model," M.S. dissertation, Univ. Maryland, 1980.
[3] A. W. Bailey, "Automatic evolution of neural net architectures," inProc. IJCNN, vol. 1, pp. 589-592, June 1990.
[4] S. Brocklehurstet al., "Recalibrating software reliability models,"IEEE Trans. Software Eng., vol. 16, pp. 458-470, Apr. 1990.
[5] L. H. Crow, "Reliability for complex repairable systems,"Reliability and Biometry, SIAM, pp. 379-410, 1974.
[6] J. L. Elman, "Finding structure in time,"Cognitive Science, pp. 179-211, 1990.
[7] S. E. Fahlman and C. Lebiere, "The cascaded-correlation learning architecture," School of Computer Science, Carnegie Mellon Univ.,Tech. Rep. CMU-CS-90-100, Feb. 1990.
[8] A. L. Goel, "Software reliability models: Assumptions, limitations, and applicability,"IEEE Trans. Software Eng., vol. SE-11, pp. 1411-1423, Dec. 1985.
[9] J. J. Hopfield and D. W. Tank, "Computing with neural circuits: A model,"Science, vol. 23, pp. 625-633, Aug. 1986.
[10] M. I. Jordan, "Attractor dynamics and parallelism in a connectionist sequential machine,"Proc. 8th Annual Conf. Cognitive Science, pp. 531-546, 1986.
[11] N. Karunanithiet al., "Prediction of software reliability using neural networks,"Proc. 1991 IEEE Int. Symp. Software Reliability Eng., pp. 124-130, May 1991.
[12] N. Karunanithiet al., "Applying neural networks for software reliability prediction,"IEEE Software, July 1992.
[13] B. Littlewood and J. L. Verrall, "A Bayesian reliability model with a stochastically monotone failure rate,"IEEE Trans. Reliability., vol. R-23, pp. 108-114, 1974.
[14] Y. K. Malaiya, N. Karunanithi, and P. Verma, "Predictability measures for software reliability models,"Proc. 14th IEEE Int. Computer Software and Applications Conf., pp. 7-12, Oct. 1990.
[15] Y. K. Malaiya, N. Karunanithi, and P. Verma, "Predictability of software reliability models,"IEEE Trans. Reliability, to be published in Dec. 1992.
[16] Y. K. Malaiya and P. K. Srimani, Eds.,Software Reliability Models: Theoretical Developments, Evaluation and Applications, IEEE Computer Society Press, 1990.
[17] J. Steben, L. Streletz, and R. Fariello, "Multiprocessing Computer System for Sensory Evoked Potentials and EEG Spectral Analysis for Clinical Neurophysiology Laboratory,"J. Medical Systems, Vol. 9, May/June 1985, pp. 347-363.
[18] P. B. Moranda, "Predictions of software reliability during debugging,"Proc. of Annual Reliability and Maintainability Symp., pp. 327-332, 1975.
[19] J. D. Musaet al., Software Reliability Measurement, Prediction, Application. New York: McGraw-Hill International, 1987.
[20] J. Moody, "Fast learning in multi-resolution hierarchies,"Advances in Neural Information Processing Systems 1., D.S. Touretzky, Ed. San Mateo, CA: Morgan Kaufman, 1989, pp. 29-39.
[21] M. Ohba, "Software reliability analysis models,"IBM J. Res. Develop., vol. 21, no. 4, pp. 428-443, July 1984.
[22] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representation by error propagation,"Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vols. 1 and 2. Cambridge, MA: MIT Press, 1986.
[23] R. Shadmehr and D. Z. D'Argenio, "A comparison of a neural network based estimator and two statistical estimators in a sparse and noisy data environment," inProc. IJCNN, vol. I, pp. 289-292, June 1990.
[24] R. Sharda and R. B. Patil, "Neural networks as forecasting experts: An empirical test," inProc. IJCNN, vol. II, pp. 491-494, June 1990.
[25] M. L. Shooman, "Probabilistic models for software reliability prediction,"Statistical Computer Performance Evaluation. New York: Academic, 1972, pp. 485-502.
[26] N. D. Singpurwalla and R. Soyer, "Assessing (software) reliability growth using a random coefficient autoregressive process and its ramifications,"IEEE Trans. Software Eng., vol. SE-11, pp. 1456-1464, Dec. 1985.
[27] R. S. Sutton, "Learning to predict by the methods of temporal differences,"Machine Learning, vol. 3, pp. 9-44, 1988.
[28] M. Takeda and J. W. Goodman, "Neural networks for computation: Number representations and programming complexity,"Applied Optics, vol. 25, pp. 3033-3045, Sept. 1986.
[29] M. F. Tenorio and Wei-Tsih Lee, "Self organizing neural networks for the identification problem,"Advances in Neural Information Processing Systems 1, D.S. Touretzky, Ed. San Mateo: Morgan Kaufman, 1989, pp. 57-64.
[30] Y. Tohmaet al., "Structural approach to the estimation of the number of residual software faults based on the hyper-geometric distribution,"IEEE Trans. Software Eng., vol. 15, pp. 345-355, Mar. 1989.
[31] Y. Tohmaet al., "Parameter estimation of the hyper-geometric distribution model for real test/debug data," Dept. Computer Science, Tokyo Inst. Tech.,Tech. Rep. 901002, 1990.
[32] A. S. Weigendet al., "Predicting the future: A connectionist approach," Stanford Univ.,Tech. Rep. Stanford-PDP-90-01, Apr. 1990.
[33] P. Werbos, "Generalization of backpropagation with application to recurrent gas market model,"Neural Networks, vol. 1, pp. 339-356, 1988.
[34] D. Whitleyet al., "Genetic algorithms and neural networks: Optimizing connections and connectivity,"Parallel Computing, vol. 14, pp. 347-361, 1990.
[35] D. Whitley and N. Karunanithi, "Improving generalization in feed-forward neural networks,"Proc. IJCNN, vol. II, pp. 77-82, July 1991.
[36] R. J. Williams and D. Zipser, "A learning algorithm for continually running fully recurrent neural networks,"Neural Computation, vol. 1, pp. 270-280, 1989.
[37] S. Yamadaet al., "S-shaped reliability growth modeling for software error detection,"IEEE Trans. Reliability., vol. R-32, pp. 475-478, Dec. 1983.

Index Terms:
software reliability; connectionist models; network models; training regimes; data representation methods; complexity; neural nets; software reliability
Citation:
N. Karunanithi, D. Whitley, Y.K. Malaiya, "Prediction of Software Reliability Using Connectionist Models," IEEE Transactions on Software Engineering, vol. 18, no. 7, pp. 563-574, July 1992, doi:10.1109/32.148475
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