CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Improving Fault Prediction using ANN-PSO in Object Oriented Systems

by Kayarvizhy N, Kanmani S, Rhymend Uthariaraj V
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 3
Year of Publication: 2013
Authors: Kayarvizhy N, Kanmani S, Rhymend Uthariaraj V
10.5120/12721-9556

Kayarvizhy N, Kanmani S, Rhymend Uthariaraj V . Improving Fault Prediction using ANN-PSO in Object Oriented Systems. International Journal of Computer Applications. 73, 3 ( July 2013), 18-25. DOI=10.5120/12721-9556

@article{ 10.5120/12721-9556,
author = { Kayarvizhy N, Kanmani S, Rhymend Uthariaraj V },
title = { Improving Fault Prediction using ANN-PSO in Object Oriented Systems },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 3 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 18-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number3/12721-9556/ },
doi = { 10.5120/12721-9556 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:39:04.217069+05:30
%A Kayarvizhy N
%A Kanmani S
%A Rhymend Uthariaraj V
%T Improving Fault Prediction using ANN-PSO in Object Oriented Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 3
%P 18-25
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Object oriented software metrics are computed and used in predicting software quality attributes of object oriented systems. Mapping software metrics to software quality attributes like fault prediction is a complex process and requires extensive computations. Many models have been proposed for fault prediction. Since accuracy is of prime importance in prediction models they are being constantly improved through various research studies. Artificial Neural network (ANN) has gained immense popularity due to its adaptability to the problem at hand by training with known data. Back propagation is a widely used ANN training technique. However the back propagation technique leads to slow convergence rate and an impending threat of getting caught in local minima. In this paper we explore the Particle Swarm Optimization (PSO) technique as an alternative to optimize the weights of ANN for fault prediction in object oriented systems. We evaluate the effect on prediction accuracy that PSO brings to ANN compared to other techniques like BP and Genetic Algorithm (GA). We also evaluate prediction accuracy improvements by optimizing the various parameters of PSO.

References
  1. Bellini P. (2005), "Comparing Fault-Proneness Estimation Models", 10th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS'05), China, pp. 205-214.
  2. Khosgoftaar TM, Gao K, Szabo RM (2001) An Application of zero-inflated poisson regression for software fault prediction, Proceedings of 12th international symposium on software reliability engineering, pp:63-73
  3. Eman K, Benlarbi S, Goel N, Rai S (2001). Comparing case-based reasoning classifiers for predicting high risk software components. Journal of System software 55(3):301-310
  4. Khosgoftaar TM, Seliya N (2002). Tree-based software quality estimation models for fault prediction. METRICS 2002. 8th IIIE Symposium on Software Metrics. pp:203-214
  5. Munson J, Khoshgoftaar T (1990). Regression Modeling of Software Quality: An Empirical Investigation. J. Info. Software Technol. 32(2):106-114.
  6. Pai G. J Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods, IEEE transaction on software Engineering, oct 2001, 33(10) :675-686
  7. K. Elish, M. Elish, "Predicting defect-prone software modules using support vector machines," Journal of System and Software, vol. 81, 649-660.
  8. T. M. Khoshgaftaar, E. D. Allen, J. Deng, Using regression trees to classify fault-prone software modules," IEEE Transactions on Reliability, vol. 51, no. 4, 455–462, 2002.
  9. [A]K. K. Aggarwal, Yogesh Singh, Arvinder Kaur, and Ruchika Malhotra "Application of Artificial Neural Network for Predicting Maintainability using Object- Oriented Metrics " World Academy of Science, Engineering and Technology 22 2006
  10. [B]S. Kanmani, V. R. Uthariaraj, V. Sankaranarayanan, and P. Thambidurai, "Object-oriented software fault prediction using neural networks," Information and Software Technology, vol. 49, 483–492, 2007.
  11. [C]K. K. Aggarwal, Y. Singh, A Kaur, R. Malhotra, "Application of Artificial Neural Network for Predicting Fault Proneness Models," in International Conference on Information Systems, Technology and. Management (ICISTM 2007), March 12-13, New Delhi, India, 2007.
  12. [D]Khoshgaftaar, T. , M. , Allen, E. , D. , Hudepohl, J. , P. , Aud, S. , J. , Application of neural networks to software quality modeling of a very large telecommunications system, IEEE Transactions on Neural Networks, vol. 8, no. 4, 902-909, 1997.
  13. [No ref]Rumelhart, D. E. , Hinton, G. E. , Williams, R. J. : Learning Representations by Back propagating Errors. Nature. 323 (1986) 533-536
  14. [F]Sexton, R. S. , Dorsey, R. E. : Reliable classification using neural networks: A genetic algorithm and back propagation comparison. Decision support systems. 30 (2000) 11-22
  15. [G]Yang. J. M. , Kao. C. Y. : A Robust Evolutionary Algorithm for Training Neural Networks. Neural Computing and Application. 10 (2001) 214-230
  16. [H]Franchini, M. : Use of A Genetic Algorithm Combined with A Local Search Method for the Automatic Calibration of Conceptual Rainfall-runoff Models. Hydrological Science Journal. 41 (1996) 21-39
  17. [I]E. I. Altman, G. Marco, and F. Varetto, "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," J. Bank. Finance, vol. 18, pp. 505–529, 1994.
  18. [J]R. C. Lacher, P. K. Coats, S. C. Sharma, and L. F. Fant, "A neural network for classifying the financial health of a firm," Eur. J. Oper. Res. , vol. 85, pp. 53–65, 1995.
  19. [k]K. Y. Tam and M. Y. Kiang, "Managerial application of neural networks: The case of bank failure predictions," Manage. Sci. , vol. 38, no. 7, pp. 926–947, 1992.
  20. [L]I. Guyon, "Applications of neural networks to character recognition," Int. J. Pattern Recognit. Artif. Intell. , vol. 5, pp. 353–382, 1991.
  21. [M]S. Knerr, L. Personnaz, and G. Dreyfus, "Handwritten digit recognition by neural networks with single-layer training," IEEE Trans. Neural Networks, vol. 3, pp. 962–968, 1992.
  22. [N]T. Petsche, A. Marcantonio, C. Darken, S. J. Hanson, G. M. Huhn, and I. Santoso, "An autoassociator for on-line motor monitoring," in Industrial Applications of Neural Networks, F. F. Soulie and P. Gallinari, Eds, Singapore: World Scientific, 1998, pp. 91–97.
  23. [O]J. Lampinen, S. Smolander, and M. Korhonen, "Wood surface inspection system based on generic visual features," in Industrial Applications of Neural Networks, F. F. Soulie and P. Gallinari, Eds, Singapore: World Scientific, 1998, pp. 35–42.
  24. [P]E. B. Barlett and R. E. Uhrig, "Nuclear power plant status diagnostics using artificial neural networks," Nucl. Technol. , vol. 97, pp. 272–281, 1992.
  25. [Q]J. C. Hoskins, K. M. Kaliyur, and D. M. Himmelblau, "Incipient fault detection and diagnosis using artificial neural networks," in Proc. Int. Joint Conf. Neural Networks, 1990, pp. 81–86.
  26. [R]K. S. Narendra and K. Parthasarathy, "Identification and Control of Dynamical Systems Using Neural Networks", IEEE Transactions on Neural Networks, Vol. 1, No. 1, March 1990, pp 4-27.
  27. [S]S. V. Kartalopoulos, Understanding Neural Networks and Fuzzy Logic, IEEE Press, 1996, ISBN 0-7803-1128-0.
  28. [T]X. Hu, Y. Shi and R. Eberhart, "Recent Advances in Particle Swarm", Proceedings of the Congress on Evolutionary Computation, Portland, OR, USA, June 19-23, 2004, Vol. 1, pp 90-97.
  29. [U]Reynolds P. D. , Duren R. W. , Trumbo M. L. and Marks R. J. , "FPGA Implementation of particle swarm optimization for inversion of large neural networks," Proc. IEEE Swarm Intelligence Symposium, SIS, pages 389-392, 2005.
  30. [V]Sun W. , Zhang Y. , and Li F. , The neural network model based on pso for short-term load forecasting, International conference on Machine Learning and Cybernetics, pp 3069-3072, 2006.
  31. [W]Cai, X. , Zhang, N. , Venayagamoorthy, G. , K. , Wunsch, D. , C. , Time series prediction with recurrent neural networks using a hybrid pso-ea algorithm, Proceedings of IEEE International Joint Conference in Neural Networks, pp 1647-1652, 2004.
  32. [X]Hatem Abdul-kader and Mustafa Abdul Salam "Evaluation of Differential Evolution and Particle Swarm Optimization Algorithms at Training of Neural Network for Stock Prediction" International Arab Journal of e-Technology, Vol. 2, No. 3, January 2012 145-150
  33. [Y]Nenortaite J. and Simutis R. , "Application of Particle Swarm Optimization Algorithm to Stocks" Trading System," 2004
  34. [Z]Ebru Ardil. , Parvinder S. Sandhu. , "A soft computing approach for modeling of severity of faults in software systems," International Journal of Physical Sciences Vol. 5(2), pp. 074-085, Feb 2010
  35. [AA]Kanu Sharma. , Navpreet Kaur. ,Sunil Khullar and Harish Kundra. , " Defect prediction based on quantitative and qualitative factors using PSO optimized neural network," International Journal of Computer Science and Communication. Vol. 3, No. 1, Jan-June 2012, pp. 33-35
  36. [BB]Kewen, Li. , Jisong, Kou, Lina, Gong. , "Predicting Software Quality by Optimized BP Network Based on PSO," Journal of Computers, Vol. 6, No. 1, Jan 2011
  37. [CC]Promise dataset for object oriented systems – metrics and bug data, http://www. promisedata. org
  38. Code Project, Genetic Algorithms in Artificial Neural Network Classification Problems, http://www. codeproject. com/Articles/21231/Genetic-Algorithms-in-Artificial-Neural-Network-Cld
Index Terms

Computer Science
Information Sciences

Keywords

swarm intelligence particle swarm optimization object oriented metrics artificial intelligence software quality

Powered by PhDFocusTM