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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29912


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9230
Recurrent Radial Basis Function Network for Failure Time Series Prediction
Abstract:
An adaptive software reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available software failure time data, Fuzzy Min-Max algorithm is used to globally optimize the number of the k Gaussian nodes. The corresponding optimized neural network architecture is iteratively and dynamically reconfigured in real-time as new actual failure time data arrives. The performance of our proposed approach has been tested using sixteen real-time software failure data. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to next-steppredictability compared to existing neural network model for failure time prediction.
Digital Object Identifier (DOI):

References:

[1] Adnan, W.A., Yaacob, M.H., 1994. An integrated neural-fuzzy system of software reliability prediction. In: Proceedings of the 1st International Conference on Software Testing, Reliability and Quality Assurance. pp. 154-158.
[2] Adnan, W.A., Yaacob, M.H., Anas, R., Tamjis, M.R., 2000. Artificial neural network for software reliability assessment. In: 2000 TENCON Proceedings of Intelligent Systems and Technologies for the New Millennium. pp. 446-451.
[3] Aljahdali, S.H., Sheta, A., Rine, D., 2001. Prediction of software reliability: a comparison between regression and neural network nonparametric models. In: Proceedings of ACS/IEEE International Conference on Computer Systems and Applications. pp. 470-473.
[4] Aljahdali, S.H., Sheta, A., Rine, D., 2002. Predicting accumulated faults in software testing process using radial basis function network models. In: Proceedings of the 17th International Conference on Computers and their Applications. pp. 26-29.
[5] Cai, K.Y., Cai, L., Wang, W.D., Yu, Z.Y., Zhang, D., 2001. On the neural network approach in software reliability modeling. The Journal of Systems and Software 58 (1), 47-62.
[6] Cai, K.Y., Wen, C.Y., Zhang, M.L., 1991. A critical review on software reliability modeling. Reliability Engineering and System Safety 32 (3), 357-371.
[7] Chappelier J.C., Grumbach A., «A Kohonen Map for Temporal Sequences», Proceeding of neural Networks and Their Application, NEURAP'96, IUSPIM, Marseille, mars 1996, p. 104-110.
[8] Chua, C.G., Goh, A.T.C., 2003. A hybrid Bayesian back-propagation neural network approach to multivariate modeling. International Journal for Numerical and Analytical Methods in Geomechanics 27(8),651-667.
[9] Elman J.L., « Finding Structure in Time », Cognitive Science, vol. 14, juin 1990, p. 179-211.
[10] Fahlman, S.E., Lebiere, C., 1990. The cascade-correlation learning architecture. Technical Report CMU-CS-90-100, School of Computer Science, Carnegie Mellon University.
[11] Ho, S.L., Xie, M., Goh, T.N., 2003. A study of the connectionist models for software reliability prediction. Computers and Mathematics with Applications 46 (7), 1037-1045.
[12] Hochman, R., Khoshgoftaar, T.M., Allen, E.B., Hudepohl, J.P., 1996. Using the genetic algorithm to build optimal neural networks for faultprone module detection. In: Proceedings of the 7th International Symposium on Software Reliability Engineering. pp. 152-162.
[13] Hochman, R., Khoshgoftaar, T.M., Allen, E.B., Hudepohl, J.P., 1997. Evolutionary neural networks: a robust approach to software reliability problems. In: Proceedings of the 8th International Symposium on Software Reliability Engineering. pp. 13-26.
[14] Karunanithi, N., Whitley, D., Malaiya, Y.K., 1992a. Prediction of software reliability using connectionist models. IEEE Transactions on Software Engineering 18 (7), 563-574.
[15] Karunanithi, N., Whitley, D., Malaiya, Y.K., 1992b. Using neural networks in reliability prediction. IEEE Software 9 (4), 53-59.
[16] Kohonen T., Self-organised formation of topologically correct feature maps, Biol. Cybern. 43 (1982) 59-69 (reprinted in Anderson and Rosen.eld, 1988).
[17] Leung, F.H.F., Lam, H.K., Ling, S.H., Tam, P.K.S., 2003. Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Transactions on Neural Networks 14 (1), 79-88.
[18] Park, J.Y., Lee, S.U., Park, J.H., 1999. Neural network modeling for software reliability prediction from failure time data. Journal of Electrical Engineering and Information Science 4 (4), 533-538.
[19] Sitte, R., 1999. Comparison of software-reliability-growth predictions: neural networks vs. parametric-recalibration. IEEE Transactions on Reliability 48 (3), 285-291.
[20] Tsoi C.T., Back A.D., « Locally Recurrent Globally Feedforward Networks : A Critical Review of Architectures », IEEE Transaction on Neural Networks Vol.05, pp. 229-239, 1994.
[21] Tsoukalas, L.H., Uhrig, R.E., 1996. Fuzzy and Neural Approaches in Engineering. Practical Aspects of Using Neural Networks. John Wiley & Sons, New York, Chapter 11, pp. 385-405.
[22] Utkin, L.V., Gurov, S.V., Shubinsky, M.I., 2002. A fuzzy software reliability model with multiple-error introduction and removal. International Journal of Reliability, Quality and Safety Engineering 9 (3), 215-227.
[23] Zemouri, R., Patic P.C., The effect of different basis functions for system output prediction, 15th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA-2010, September 13-16, 2010, Bilbao Spain (Submitted for publication).
[24] Zemouri, R., Patic P.C., Prediction Error Feedback for Time Series Prediction: a way to improve the accuracy of predictions, Proceedings of the 4th EUROPEAN COMPUTING CONFERENCE (ECC '10), April 20-22, 2010, Bucharest, Romania, p. 58-62, ISSN 1790-5117, ISBN 978-960-474-178-6.
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