COMPARISON BETWEEN VARIOUS OPTIMIZATION TECHNIQUES FOR FINDING ADEQUATE TEST CASES

COMPARISON BETWEEN VARIOUS OPTIMIZATION TECHNIQUES FOR FINDING ADEQUATE TEST CASES
Author(s):
Anamika Madaan, Bharti Jha
Keywords:
Ant colony optimization,Evolutionary Algorithm, Genetic algorithm, Particle swarm optimization algorithm, Software Testing, Test Cases, Swarm intelligence.
Abstract
Software Testing is the most important activity in the developmenmt of software. It ensures the development of a high quality software. Test cases are needed in software testing to find out whether the software is error free or not. Hence, this paper focuses on the comparison between three genetic algorithm, ant colony optimization and particle swarm optimization algorithm for finding adequate test cases. The basic principle of Genetic algorithm is the “survival of the fittest”. Ant Colony optimization(ACO)algorithm is inspired from the behaviour of ants. ACO is a heuristic algorithm. Particle swarm optimization is also an optimization algorithm which is inspired by flocks of birds or herds of animals who find their food by a method of “information sharing”. In this paper, These algorithms are compared to find out that which algorithm is the most efficient among them.
Article Details
Unique Paper ID: 143646 Publication Volume & Issue: Volume 2, Issue 12 Page(s): 402 - 406
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