The Transactions of the Korea Information Processing Society (한국정보처리학회논문지)

Korea Information Processing Society (한국정보처리학회)
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Neural Network for Softwar Reliability Prediction ith Unnormalized Data

비정규화 데이터를 이용한 신경망 소프트웨어 신뢰성 예측

  • 이상운 (경상대학교 대학원 전자계산학과)
  • Published : 2000.05.01
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Abstract

When we predict of software reliability, we can't know the testing stopping time and how many faults be residues in software the (the maximum value of data) during these software testing process, therefore we assume the maximum value and the training result can be inaccuracy. In this paper, we present neural network approach for software reliability prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data.

Keywords

References

  1. S. Bos, 'How to Partition Examples between Cross-Validation Set, and Training Set?,' Lab. for Information Representation, RIKEN, 1996
  2. G. Cybenko, 'Approximation by Super-positions of A Sigmoidal Function,' Mathematics of Control, Signals and Systems, Vol.2, pp.303-314, 1989 https://doi.org/10.1007/BF02551274
  3. J. L. Elman, 'Finding Structure in Time,' Cognitive Science, pp.179-211, 1990
  4. A. L. Gael, 'Software Reliability Models Assumptions, Limitation, and Applicability,' IEEE Trans. on Software Eng. Vol.SE-11, No.12, pp.1411-1423, 1985 https://doi.org/10.1109/TSE.1985.232177
  5. L. Holmstrom, P. Koistinen, J. Laaksonen, and E. Oja, 'Neural and Statistical Classifiers-Taxonomy and Two Case Studies,' IEEE Trans. on Neural Networks, Vol.8, No.1, pp.5-17, 1997 https://doi.org/10.1109/72.554187
  6. R. H. Hou and S. Y. Kuo, 'Applying Various Learning Curves to Hypergeometric Distribution Software Reliability Growth Model,' IEEE. 1994 https://doi.org/10.1109/ISSRE.1994.341342
  7. M. L. Jordan, 'Attractor Dynamics and Parallelism in a Connectionist Sequential Machine,' Proc. 8th Annual Cognitive Science, pp.531-546, 1986
  8. N. Karumanithi, D. Whitley, and Y. K. Malaiya, 'Prediction of Software Reliability Using Connectionist Models,' IEEE Trans. on Software Eng., Vol. 18, No.7, pp.563-574, July. 1992 https://doi.org/10.1109/32.148475
  9. K. Karunanithi, D. Whitley and Y. K. Malaiya, 'Using Neural Networks in Reliability Prediction,' IEEE Software., pp.53-59, 1997 https://doi.org/10.1109/52.143107
  10. T. M. Khoshgoftaar, E. B. Allen, J. P. Hudepohi, and S. J Aud, 'Application of Neural Networks to Software Quality Modeling of a very Large Telecommunications Systems,' IEEE Trans. on Neural Networks, Vol.8, No.4, pp.902-909, 1997 https://doi.org/10.1109/72.595888
  11. M. R. Lyu, 'Handbook of Software Reliability Engineering,' IEEE Computer Society Press, 1996
  12. J. D. Musa, 'Software Reliability Data,' Technical Report, Data and Analysis Center for Software, Rome Air Development Center, Griffins AFB, New York, 1979
  13. J. Y. Park, S. U. Lee, and J. H. Park, 'Neural Network Modeling for Software Reliability Prediction from Failure Time Data,' Journal of Electrical Eng. and Information Science, Vol.4, No.4, pp.533-538, 1999
  14. F. Popentiu and D. N. Boros, 'Software Reliability Growth Supermodels,' Microelectron. Reliab. Vol. 36, No.4, pp.485-491, 1996 https://doi.org/10.1016/0026-2714(95)00068-2
  15. Y. Thoma et al., 'Parameter Estimation of the Hyper-Geometric Distribution Model for Real Test/Debug Data,' Dept. Computer Science, Tokyo Inst. Tech., Tech. REP. 901002, 1990
  16. R. J. Williams and D. Zipser, 'A Learning Algorithm for Continuous Running Fully Recurrent Neural Networks,' Neural Computation, Vol.1, pp. 270-280, 1989