CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Exploring Ant Lion Optimization Algorithm to Enhance the Choice of an Appropriate Software Reliability Growth Model

by Marrwa Abd-AlKareem Alabajee, Taghreed Riyadh Alreffaee
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 4
Year of Publication: 2018
Authors: Marrwa Abd-AlKareem Alabajee, Taghreed Riyadh Alreffaee
10.5120/ijca2018917499

Marrwa Abd-AlKareem Alabajee, Taghreed Riyadh Alreffaee . Exploring Ant Lion Optimization Algorithm to Enhance the Choice of an Appropriate Software Reliability Growth Model. International Journal of Computer Applications. 182, 4 ( Jul 2018), 1-8. DOI=10.5120/ijca2018917499

@article{ 10.5120/ijca2018917499,
author = { Marrwa Abd-AlKareem Alabajee, Taghreed Riyadh Alreffaee },
title = { Exploring Ant Lion Optimization Algorithm to Enhance the Choice of an Appropriate Software Reliability Growth Model },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2018 },
volume = { 182 },
number = { 4 },
month = { Jul },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number4/29747-2018917499/ },
doi = { 10.5120/ijca2018917499 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:10:19.921587+05:30
%A Marrwa Abd-AlKareem Alabajee
%A Taghreed Riyadh Alreffaee
%T Exploring Ant Lion Optimization Algorithm to Enhance the Choice of an Appropriate Software Reliability Growth Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 4
%P 1-8
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software reliability always related to software failures, in a past few decades a software reliability growth models (SRGMs) number have been developed to predict the software reliability under different environment, but there is no single model that best fits all the real life situations and so can be recommended universally. to predict the failures of software accurately, an appropriate and best model must be chosen, this will help to estimate the cost and delivery time of the project. In this paper, Ant Lion optimization (ALO) algorithm is proposed to optimize estimation of parameters and a choice procedure is used to select an appropriate model of the software reliability that best fit available dataset of an ongoing projects of the software. Employing ALO algorithm for estimating the SRGM’s parameters has provided more accurate prediction and enhance procedure of the selection, making a decision to select suitable SRGMs during the phases of the testing can be more easier to a developer of the software .The explored algorithm has been  examined on various datasets of software projects and it has been noticed that this method is better than other methods proposed.

References
  1. Diwaker, C. and Goyat, S. 2014. Parameter Estimation of Software Reliability Growth Models Using Simulated Annealing Method, International Journal of Computer Applications Technology and Research Volume (3), Issue (6), pp:377 - 380.
  2. Al Turk, L. I. and Alsolami, E. G. 2016. JELINSKI-MORANDA SOFTWARE RELIABILITY GROWTH MODEL: A BRIEF LITERATURE AND MODIFICATION, International Journal of Software Engineering & Applications (IJSEA), Vol.( 7), No.(2).
  3. ONG, L. F., ISA, M. A., JAWAWI, D. N. A. and ABDUL HALIM, S. 2017. improving software reliability growth model selection ranking using particle swarm optimization, Journal of Theoretical and Applied Information Technology, 15th January 2017,Vol.(95), No.(1) .
  4. Iqbal, J. 2016. Analysis of Some Software Reliability Growth Models with Learning Effects, I.J. Mathematical Sciences and Computing, 2016, 3, pp:58-70, Published Online July 2016 in MECS (http://www.mecs-press.net) DOI: 10.5815/ijmsc.2016.03.06.
  5. Miglani, N. 2014. On the Choice of an Appropriate Software Reliability Growth Model, International Journal of Computer Applications (0975 – 8887) Volume (87), No.(9).
  6. Hsu, C. J. and Huang, C.Y. 2010. A Study on the Applicability of Modified Genetic Algorithms for the Parameter Estimation of Software Reliability Modeling, IEEE 34th Annual Computer Software and Applications Conference, pp:531-540.
  7. Shanmugam, L. and Florence, L. 2012. Comparison of Parameter Best Estimation Method for Software Reliablity Models, International Journal of Software Engineering & Applications (IJSEA).
  8. AL-Saati, N. A. and Abd-AlKareem M. 2013. The Use of Cuckoo Search in Estimating the Parameters of Software Reliability Growth Models,(IJCSIS) International Journal of Computer Science and Information Security, Vol. (11), No.(6).
  9. Diwaker, C. and Goyat, S. 2014. Parameter Estimation of Software Reliability Growth Models Using Simulated Annealing Method, International Journal of Computer Applications Technology and Research, Volume (3), Issue (6), pp:377 – 380.
  10. Mallikharjuna Rao K. and Anuradha, K. 2016 . A New Method to Optimize the Reliability of Software Reliability Growth Models using Modified Genetic Swarm Optimization, International Journal of Computer Applications (0975 – 8887), Volume (145), No.(5).
  11. Sheta, A. F. and Abdel-Raouf, A. 2016. Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol.(7), No.(4).
  12. Alneamy, J. S. M. and Dabdoob, M. M. A. 2017. The Use of Original and Hybrid Grey Wolf Optimizer in Estimating the Parameters of Software Reliability Growth Models, International Journal of Computer Applications (0975 – 8887), Volume (167) , No.(3).
  13. Song,K. Y., Chang, I. H. and Pham, H. 2017. An NHPP Software Reliability Model with S-Shaped Growth Curve Subject to Random Operating Environments and Optimal Release Time, Applied Sciences, 2017, 7, 1304; doi:10.3390/ app7121304.
  14. Kaur, R. and Panwar, p. 2015. Study of Perfect and Imperfect Debugging NHPP SRGMs used for Prediction of Faults in a Software, IJCSC,Vol. (6), No. (1),pp:73-78.
  15. Aggarwal, G. and Gupta, V. K. 2014. Software Reliability Growth Model, International Journal of Advanced Research in Computer Science and Software Engineering, Volume(4), Issue (1).
  16. Singh, M. and Bansal, V. 2015. Parameter Estimation and Validation Testing Procedures for Software Reliability Growth Model, International Journal of Science and Research (IJSR), Volume (5), Issue(12).
  17. Petrović, M., Petronijević, J., Mitić, M., Vuković, N., Plemić, A., Miljković, Z. and Babić, B. 2015. The Ant Lion Optimization Algorithm For Flexible Process Planning, journal of production engineering, vol .( 18),No.(2).
  18. Mirjalili, S. 2015. The Ant Lion Optimizer, Advances in Engineering Software, 83 (2015), pp: 80–98.
  19. Satheeshkumar, R. and Shivakumar, R. 2016. Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems, journal of scientific research publishing, 7, 2357-2383.
  20. Talatahari, S. 2016. Optimum Design Of Skeletal Structures Using Ant Lion Optimizer, International Journal Of Optimization In Civil Engineering, 6(1),pp:13-25.
  21. Nischal, M. M. and Mehta, S. 2015. Optimal Load Dispatch Using Ant Lion Optimization, Int. Journal of Engineering Research and Applications, ISSN: 2248-9622, Vol.(5), Issue(8), (Part - 2) August 2015, pp.10-19.
  22. Ali, E. S., AbdElazima, S. M. and Abdelaziz, A. Y. 2017. Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations, Renewable Energy, Vol.(101) pp:1311-1324.
  23. Mohd, R. and Nazir, M. 2012. Software Reliability Growth Models: Overview and Applications, Journal of Emerging Trends in Computing and Information Sciences, VOL. (3), NO. (9).
  24. Jiang, R. 2009. Required Characteristics for Software Reliability Growth Models. In World Congress on Software Engineering IEEE 2009. DOI 10.1109/WCSE.2009.157
Index Terms

Computer Science
Information Sciences

Keywords

Software Reliability Ant Lion Optimization Algorithm Software Reliability Growth Models.

Powered by PhDFocusTM