An Effective Meta-Heuristic Cuckoo Search Algorithm for Test Suite Optimization
Abstract
Automation testing is the process of generating test data programmatically without
any human interventions. In recent times, nature-inspired solutions are planned, tested and validated successfully in many areas for the purpose of optimization. One such metaheuristic technique is Cuckoo Algorithm (CA) that receives its sole inspiration from the behavior of cuckoo, who has the ability to resolve complex issues using simple initial conditions and limited knowledge of the search space. This paper presents cost effective and time efficient algorithm inspired from cuckoo for optimizing the test data. When the proposed algorithm is compared with existing Fire y Algorithm (FA) and Hill Climbing (HC) algorithms, it was found that CA outperforms both FA and HC in terms of test data optimization process. The work done in the current study would be helpful to testers in generating optimized test data which would result in saving of both testing cost and time.
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