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An Approach in the Software Testing Environment using Artificial Bee Colony (ABC) Optimization
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 58 - Number 21 |
| Year of Publication: 2012 |
| Authors: Tajinder Singh, Mandeep Kaur Sandhu |
10.5120/9404-3662
|
Tajinder Singh, Mandeep Kaur Sandhu . An Approach in the Software Testing Environment using Artificial Bee Colony (ABC) Optimization. International Journal of Computer Applications. 58, 21 ( November 2012), 5-7. DOI=10.5120/9404-3662
Abstract
So many techniques are used in the software testing environment and in this paper we survey the ABC algorithmic approach and its advantages over the GA (Genetic Algorithms). Artificial bee colony (ABC) algorithm is one of the most recently introduced swarm–based algorithms. These optimization approaches helps in memorization and also support the global optima. This algorithm is based on colony size and the maximum number of cycles per number. In this algorithm we know that it is a optimization tool which provides the population based search procedure which is known as Food Sources and these food sources are searched by Employee Bees ABC as an optimization tool which provides a population-based search procedure in which individuals food positions are modified by different bees with time.
References
- D. Jeya Mala, V. Mohan, 2009. ABC Tester - Artificial Bee Colony Based Software Test Suit Optimization Approach, in International . Journal of Software Engineering, IJSE Vol. 2 No. 2.
- D. Karaboga, B. Basturk, 2007. On The Performance Of Artificial Bee Colony (ABC) Algorithm, Applied Soft Computing, Volume 8, Issue 1, January 2008, pp. 687-697.
- D. Karaboga, 2005. An idea based on beeswarm for numerical optimization, Tech. Rep. TR-06, Erciyes University Engineering Faculty ? Computer Engineering Department.
- F. Gao, J. -J. Lee, Z. Li, H. Tong, and X. L¨u, 2009. Parameter estimation for chaotic system with initial random noises by particle swarm optimization, Chaos, Solitons & Fractals, vol. 42, no. 2, pp. 1286–1291.
- Hélène Waeselynck, Pascale Thévenod-Fosse, Olfa Abdellatif-Kaddour, 2007. Simulated annealing applied to test generation: landscape characterization and stopping criteria, Empirical Software Engineering, Vol. 12, No. 1, pp. 35-63.
- Michael Grottke, Kishor S. Trivedi, 2007. Fighting Bugs: Remove, Retry, Replicate, and Rejuvenate, IEEE Computer, Vol. 40 No. 2, pp. 107-109.
- Mohammad Fathian, Babak Amiri and Ali Maroosi, 2007. Application of honey-bee Mating optimization algorithm on clustering, Applie Mathematics and Computation, Volume 190, Issue 2, pp. 1502-1513.
- Rudolf Ramler, Klaus Wolfmaier, 2006. Economic perspectives in automation balancing automated and manual testing with opportunity cost, Proceedings of the international workshop on Automation of Software Test, pp. 15-23.
- Tracey, N. , Clark, N. , . Mander K. , and McDermid, N. , 2002. A Search Based Automated Test Data Generation Framework for Safety Critical Systems, in Systems Engineering for Business Process Change (New Directions), Henderson P. , Editor, Springer Verlag.
- Y. Chen, X. Chen, and S. Gu, 2007. Lag synchronization of structurally nonequivalent Chaotic systems with time delays, Nonlinear Analysis: Theory, Methods & Applications, vol. 66, no. 9, pp. 1929–1937.
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