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

A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

by Prashant Johri, Md. Nasar, Udayan Chanda
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 68 - Number 5
Year of Publication: 2013
Authors: Prashant Johri, Md. Nasar, Udayan Chanda
10.5120/11575-6891

Prashant Johri, Md. Nasar, Udayan Chanda . A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort. International Journal of Computer Applications. 68, 5 ( April 2013), 21-25. DOI=10.5120/11575-6891

@article{ 10.5120/11575-6891,
author = { Prashant Johri, Md. Nasar, Udayan Chanda },
title = { A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 68 },
number = { 5 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 21-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume68/number5/11575-6891/ },
doi = { 10.5120/11575-6891 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:27:00.276941+05:30
%A Prashant Johri
%A Md. Nasar
%A Udayan Chanda
%T A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort
%J International Journal of Computer Applications
%@ 0975-8887
%V 68
%N 5
%P 21-25
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Allocation of limited testing efforts to a software development project is a complex task for software managers. The challenges become difficult when the nature of the development process is considered in the dynamic environment. Numerous software reliability growth models have been proposed in last one decade to minimize the whole testing effort expenditures, but generally under static assumption. The main purpose of this article is to distribute total testing resource optimally under dynamic condition. An elaborate optimization policy is proposed using genetic algorithm and numerical example is also demonstrated. Genetic Algorithms (GAs) works with a set of individuals, representing probable solutions of the task. The selection theory is applied by using a criterion, giving an evaluation for the individual with respect to the desired solution. This article also studies the optimal resource allocation problems for different conditions by investigative the activities of the model parameters.

References
  1. Basili, V. R. and Zelkowitz, M. V. (1979), 'Analyzing Medium Scale Software Development', in Proceedings of the 3rd International Conference on Software Engineering, pp. 116–123.
  2. Blackburn, J. D. , Scudder, G. D. , and Van Wassenhove, L. N. (2000), 'Concurrent Software Development', Communications of the ACM, 43, 200-214
  3. Chang, Y. -C. (2004), 'A Sequential Software Release Policy', Annals of the Institute of Statistical Mathematics, 56, 193–204.
  4. Chatterjee, S. , Misra, R. B. , and Alam, S. S. (1997), 'Joint Effect of Test Effort and Learning Factor on Software Reliability and Optimal Release Policy', International Journal of Systems Science, 28, 391–396.
  5. Chiang, I. R. , and Mookerjee, V. S. (2004), 'A Fault Threshold Policy to Manage Software Development Projects', Information Systems Research, 15, 3–19.
  6. Painton, L. , Campbell, J. , 1995. Genetic algorithms in optimization of system reliability. IEEE Transaction on Reliability. 44 (2), 172–178.
  7. Hou, R. H. , Kuo, S. Y. , and Chang, Y. P. (1997), 'Optimal Release Times for Software Systems with Scheduled Delivery Time Based on the HGDM', IEEE Transactions of Computer, 46, 216–221.
  8. Global Optimization Toolbox User's Guide R2012 a The MathWorks, Inc.
  9. Huang, C. -Y. , Kuo, S. -Y. and Chen, J. Y. (1997), 'Analysis of a Software Reliability Growth Model with Logistic Testing Effort Function', in Proceeding of 8th International Symposium on Software Reliability Engineering, pp. 378–388.
  10. David, L. Handbook of Genetic Algorithms. New York : Van Nostrand Reinhold. 1991
  11. Ichimori, T. , Yamada, S. and Nishiwaki, M. (1993), 'Optimal Allocation Policies for Testing-resource Based on a Software Reliability Growth Model', in Proceedings of the Australia–Japan Workshop on Stochastic Models in Engineering, Technology and Management, pp. 182–189.
  12. Lin, C. , Shen, S. , Yeh, Y. , & Ding, J. (2001). Dynamic Optimal Control Policy In Advertising Price and Quality. International Journal of Systems Science, 32, 2. Business Source Premier Database
  13. Jain, M. and Priya, K. (2002). Optimal policies for software testing time. Journal of Computer Society of India, 32, 25-30.
  14. Jain, M. and Gupta, R. (2011). Optimal Release Policy of Module-Based Software. Quality Technology and Quantitative Management Vol. 8, No. 2, pp. 147-165 Lin, C. , Shen, S. , Yeh, Y. , & Ding, J. (2001).
  15. Kosters W. A. , Kok J. N. and Floreen P. , Fourier Analysis of Genetic Algorithms, Theoretical Computer Science, Elsevier, 229, 199, pp. 143-175.
  16. Kapur, P. K. , and Bardhan, A. K. (2002), 'Testing Effort Control Through Software Reliability Growth Modelling', International Journal of Modelling and Simulation, 22, 90–96.
  17. Kapur, P. K. , and Garg, R. B. (1990), 'Cost Reliability Optimum Release Policy for a Software System with Testing Effort', OPSEARCH, 27, 109–116.
  18. Kapur, P. K. , Garg, R. B. , and Kumar, S. (1999), Contributions to Hardware and Software Reliability, Singapore: World Scientific.
  19. Kapur, P. K. , Gupta, A. , Shatnawi, O. R. , and Yadavalli, V. S. S. (2006), 'Testing Effort Control Using Flexible Software Reliability Growth Model with Change Point', International Journal of Performability Engineering, 2, 245–262.
  20. Musa, J. D. , Iannino, A. , and Okumoto, K. (1987), Software Reliability: Measurement, Prediction, Applications, New York: Mc Graw Hill.
  21. Myers, G. J. (1976), Software Reliability: Principles and Practices, New York: John Wiley & Sons.
  22. Pham, H. , and Zhang. , X. (1999), 'A Software Cost Model with Warranty and Risk Costs', IEEE Transactions on Computer, 48, 71–75.
  23. Pillai, K. , and Nair, V. S. S. (1997), 'A Model for Software Development Effort and Cost Estimation', IEEE Transations on Software Engineering, 23, 485–497.
  24. Sethi, S. P. , and Thompson, G. L. (2005), Optimal Control Theory – Applications to Management Science and Economics (2nd ed. ), New York: Springer.
  25. Tamura, Y. , and Yamada, S. (2009), 'Optimisation Analysis for Reliability Assessment Based on Stochastic Differential Equation Modelling for Open Source Software', International Journal of Systems Science, 40, 429–438.
  26. Xie, M. (1991), Software Reliability Modeling, Singapore: World Scientific.
  27. Yamada, S. , Hishitani, J. , and Osaki, S. (1993), 'Software Reliability Growth Model with Weibull Testing Effort: A Model and Application', IEEE Transactions on Reliability, R-42, 100–105.
  28. Zheng, S. (2002). Dynamic release policies for software systems with a reliability constraint. IIE Transactions, 34, 253-262.
  29. Goldberg, D. E. , 1989, Genetic Algorithms: in Search Optimization and Machines Learning (New York: Addison-Wesley).
  30. Putnam, L. (1978), 'A General Empirical Solution to the Macro Software Sizing and Estimating Problem', IEEE Transactions on Software Engineering, SE-4, 345–361.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm testing effort allocation Software reliability SRGM

Powered by PhDFocusTM