CFP last date
22 April 2024
Reseach Article

A New Method to Optimize the Reliability of Software Reliability Growth Models using Modified Genetic Swarm Optimization

by Mallikharjuna Rao K., K. Anuradha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 5
Year of Publication: 2016
Authors: Mallikharjuna Rao K., K. Anuradha
10.5120/ijca2016910610

Mallikharjuna Rao K., K. Anuradha . A New Method to Optimize the Reliability of Software Reliability Growth Models using Modified Genetic Swarm Optimization. International Journal of Computer Applications. 145, 5 ( Jul 2016), 1-8. DOI=10.5120/ijca2016910610

@article{ 10.5120/ijca2016910610,
author = { Mallikharjuna Rao K., K. Anuradha },
title = { A New Method to Optimize the Reliability of Software Reliability Growth Models using Modified Genetic Swarm Optimization },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 5 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number5/25271-2016910610/ },
doi = { 10.5120/ijca2016910610 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:47:56.515108+05:30
%A Mallikharjuna Rao K.
%A K. Anuradha
%T A New Method to Optimize the Reliability of Software Reliability Growth Models using Modified Genetic Swarm Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 5
%P 1-8
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software reliability is one of the key attributes to determine the quality of a software system. Finding and minimizing the remaining faults in software systems is a challenging task. Software reliability growth model (SRGM) with testing-effort function (TEF) is very helpful for software developers and has been widely accepted and applied. However, each SRGM with TEF (SRGMTEF) contains some undetermined parameters. Optimization of these parameters is a necessary task. Generally, these parameters are estimated by the Least Square Estimation (LSE) or the Maximum Likelihood Estimation (MLE). However, the software failure data may not satisfy such a distribution. We investigate the improvement and application of a swarm intelligent optimization algorithm, namely Modified Genetic Swarm Optimization algorithm, to optimize these parameters of SRGMTEF. The performance of the proposed SRGMTEF model with optimized parameters is also compared with other existing models Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The experiment results show that the proposed parameter optimization approach using Modified Genetic Swarm Optimization is very effective and flexible, and the better software reliability growth performance can be obtained based on SRGMTEF on the different software failure datasets. Also, provided comparison of ten SRGMs ( Like Goel-Okumoto Model, Delayed S-shaped Growth Model, Yamada Imperfect Debugging Models, Yamada Rayleigh Model, Inflection S-shaped Model…..etc).

References
  1. M.R.Lyu, “Handbook of Software Reliability Engineering,” Mc Graw Hill, 1996
  2. Kapil Sharma, Rakesh Garg, C.K. Nagpal, and R.K.Garg, ”Selection of Optimal Software Reliability Growth Models Using a Distance Based Approach,” IEEE Transactions on Reliability, Vol. 59, No.2, pp 266-276, June 2010.
  3. Chin-Yu Huang, and Michael R. Lyu, “Estimation and Analysis of Some Generalized Multiple Change-Point Software Reliability Models,” IEEE Transactions on Reliability, Vol. 60, No.2, pp 498- 514, June 2011.
  4. P.K. Kapur, H.Pham, Sameer Anand, and Kalpana Yadav,” A Unified Approach for Developing Software Reliability Growth Models in the Presence of Imperfect Debugging and Error Generation,” IEEE Transactions on Reliability, Vol. 60, No.1, pp. 331-340, March 2011.
  5. Y.P. Wu, Q,P.Hu, M.Xie, and S.H. Ng, “ Modeling and Analysis of Software Fault Detection and Correction Process by Considering Time Dependency,” IEEE Transactions on Reliability, Vol. 56, No. 4, pp. 629-642, December 2007.
  6. Ali Hadidi, Sina Kazemzadeh Azad, Saeid Kazemzadeh Azad, “Structural optimization using artificial bee colony algorithm”, Proc.of International Conference on Engineering Optimization, 2010.
  7. J. Kennedy and R. C. Eberhart, “Particle swarm optimization”, Proc. of IEEE International Conference on Neural Networks, Vol. 5, No. 3,pp. 1942–1948, 1995.
  8. Ali R. Yildiz, “Cuckoo search algorithm for the selection of optimal machining parameters in milling operations”, The International Journal of Advanced Manufacturing Technology, Vol.64, Issue.1-4, pp.55-61, 2012.
  9. C. Y. Huang, M.R. Lyu, and S.Y. Kuo, “ A unified scheme of some non-homogeneous Poisson process models for software reliability estimation,” IEEE Transactions on Software Engineering, Vol. 29, No.3, pp. 261-269, March 2003.
  10. Mallikharjuna Rao. K, Anuradha. Kodali, “An Efficient Method for Software Reliability Growth Model Sselection using Modified Particle Swam Optimization Technique”, International Review on Computers and Software (I.RE.CO.S), Vol. 10, No.12, pp. 1169-1178, December 2015.
  11. H.Pham, “Software reliability and cost models: perspectives, comparison and practice,” European Journal of Operational Research, Vol. 149, pp. 475-489, 2003.
  12. S. Yamada, K.Tokuno, and S.Osaki, “Imperfect debugging models with fault introduction rate for software reliability assessment,” International Journal of System Science, Vol. 23, No. 12, 1992.
  13. H.Pham, L. Nordmann, and X. Zhang, “A general imperfect software debugging model with s-shaped fault detection rate,” IEEE Transactions on Reliability, Vol. 48, pp. 169-175, June 1999.
  14. Mallikharjuna Rao. K, Kodali Anuradha, “An Efficient Method for Parameter Estimation of Software Reliability Growth Model Using Artificial Bee Colony Optimization,” In Proc. Of 5th International Conference on SEMCCO-2014, Lecture Notes in Computer Science, Springer Series, Vol. 8947, pp. 765-776, Switzerland 2015.
  15. Anupriya, Akashtayal, "Comparison of Hybrid and classical Metaheuristic for Automatic image enhancement", International Journal of Computer Applications, Vol. 46, No.2, pp. 0975 – 8887, May 2012.
  16. Apurba Gorai, Ashish Ghosh, "Hue-Preserving Color Image Enhancement Using Particle Swarm Optimization", Proc. 0f Recent Advances in Intelligent Computational Systems, pp. 563 - 568, Sept 2011.
  17. S. Yamada, H. Ohtera and R. Narihisa, "Software Reliability Growth Models with Testing-Effort," IEEE Trans. Reliability, Vol. R-35, pp.19-23 (1986).
Index Terms

Computer Science
Information Sciences

Keywords

Software Reliability Growth Model Testing Effort Function Genetic Algorithm Particle Swarm Optimization Modified Genetic Swarm Optimization. Software Reliability