CFP last date
21 October 2024
Reseach Article

Software Reliability Prediction using Neural Network with Encoded Input

by Manjubala Bisi, Neeraj Kumar Goyal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 22
Year of Publication: 2012
Authors: Manjubala Bisi, Neeraj Kumar Goyal
10.5120/7492-0586

Manjubala Bisi, Neeraj Kumar Goyal . Software Reliability Prediction using Neural Network with Encoded Input. International Journal of Computer Applications. 47, 22 ( June 2012), 46-52. DOI=10.5120/7492-0586

@article{ 10.5120/7492-0586,
author = { Manjubala Bisi, Neeraj Kumar Goyal },
title = { Software Reliability Prediction using Neural Network with Encoded Input },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 22 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 46-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number22/7492-0586/ },
doi = { 10.5120/7492-0586 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:42:34.924386+05:30
%A Manjubala Bisi
%A Neeraj Kumar Goyal
%T Software Reliability Prediction using Neural Network with Encoded Input
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 22
%P 46-52
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A neural network based software reliability model to predict the cumulative number of failures based on Feed Forward architecture is proposed in this paper. Depending upon the available software failure count data, the execution time is encoded using Exponential and Logarithmic function in order to provide the encoded value as the input to the neural network. The effect of encoding and the effect of different encoding parameter on prediction accuracy have been studied. The effect of architecture of the neural network in terms of hidden nodes has also been studied. The performance of the proposed approach has been tested using eighteen software failure data sets. Numerical results show that the proposed approach is giving acceptable results across different software projects. The performance of the approach has been compared with some statistical models and statistical models with change point considering three datasets. The comparison results show that the proposed model has a good prediction capability.

References
  1. www. thedacs. com The Data and Analysis Centre for Software DACS.
  2. M. R. Lyu, Handbook of software reliability engineering, New York: McGraw-Hill, 1996.
  3. L. K. Hansen, P. Salamon, Neural network ensembles, IEEE Transaction on pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993-1001, 1990.
  4. N. Karunanithi , Y. K. Malaiya , D. Whitley, Prediction of software reliability using neural networks, Proceedings of the Second IEEE International Symposium on Software Reliability Engineering, pp. 124–130, 1991.
  5. N. Karunanithi, D. Whitley, Y. K. Malaiya, Prediction of software reliability using connectionist models, IEEE Trans Software Engg. , vol. 18, no. 7, pp. 563-573, 1992.
  6. N. Karunanithi, D. Whitley, Y. K. Malaiya, Using neural networks in reliability prediction, IEEE Software, vol. 9, no. 4, pp. 53–59, 1992.
  7. S. D. Conte, H. E. Dunsmore, V. Y. Shen, Software Engineering Metrics and Models, Redwood City: Benjamin-Cummings publishing Co. , Inc. , 1986.
  8. R. Sitte, Comparision of software–reliability-growth predictions:neural networks vs parametric recalibration, IEEE Transactions on Reliability, vol. 48, no. 3, pp. 285-291, 1999.
  9. K. Y. Cai , L. Cai , W. D. Wang , Z. Y. Yu , D. Zhang , On the neural network approach in software reliability modeling, The Journal of Systems and Software, vol. 58, no. 1, pp. 47-62, 2001.
  10. P. M. Granotto, P. F. Verdes, H. A. Caccatto, Neural network ensembles: Evaluation of aggregation algorithms, Artificial Intelligence, vol. 163, no. 2, pp. 139-162, 2005.
  11. L. Tian, A. Noore, On-line prediction of software reliability using an evolutionary connectionist model, The Journal of Systems and Software, vol. 77, no. 2, pp. 173–180, 2005.
  12. L. Tian, A. Noore, Evolutionary neural network modeling for software cumulative failure time prediction, Reliability Engineering and System Safety, vol. 87, no. 1, pp. 45–51, 2005.
  13. Q. P. Hu, Y. S. Dai, M. Xie, S. H. Ng, Early software reliability prediction with extended ANN model, Proceedings of the 30th Annual International Computer Software and Applications Conference, pp. 234-239, 2006.
  14. S. P. K. Viswanath, Software Reliability Prediction using Neural Networks, PhD. Thesis, Indian Institute of Technology Kharagpur, 2006.
  15. Y. S. Su, C. Y. Huang, Neural-Networks based approaches for software reliability estimation using dynamic weighted combinational models, The Journal of Systems and Software, vol. 80, no. 4, pp. 606-615, 2007.
  16. B. Yang, L. Xiang, A study on Software Reliability Prediction Based on Support Vector Machine, International conference on Industrial Engineering & Engineering Management, pp. 1176-1180, 2007.
  17. S. H. Aljahdali, K. A. Buragga, Employing four ANNs paradigm for Software Reliability Prediction: an Analytical study, ICGST International Journal on Artificial Inteligence and Machine Learning, vol. 8, no. 2, pp. 1-8, 2008.
  18. J. Zheng, Predicting Software reliability with neural network ensembles, Expert systems with applications, vol. 36, no. 2, pp. 2116-2122, 2009.
  19. Y. Singh, P. Kumar, Application of feed-forward networks for software reliability prediction, ACM SIGSOFT Software Engineering Notes, vol. 35, no. 5, pp. 1-6, 2010.
  20. Y. Singh, P. Kumar, Prediction of Software Reliability Using feed Forward Neural Networks, International conference on Computational Intelligence and software Engineering, pp. 1-5, 2010.
  21. C. Y. Huang, M. R. Lyu, Estimation and Analysis of Some Generalized Multiple Change-Point Software Reliability Models, IEEE Transaction on Reliability, vol. 60, no. 2, pp. 498-514, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Failure Prediction Neural Network Encoded Input Encoded Parameter.