A non-pheromone based intelligent swarm optimization technique in software test suite optimization

In our paper, we applied a non-pheromone based intelligent swarm optimization technique namely artificial bee colony optimization (ABC) for test suite optimization. Our approach is a population based algorithm, in which each test case represents a possible solution in the optimization problem and happiness value which is a heuristic introduced to each test case corresponds to the quality or fitness of the associated solution. The functionalities of three groups of bees are extended to three agents namely Search Agent, Selector Agent and Optimizer Agent to select efficient test cases among near infinite number of test cases. Because of the parallel behavior of these agents, the solution generation becomes faster and makes the approach an efficient one. Since, the test adequacy criterion we used is path coverage; the quality of the test cases is improved during each iteration to cover the paths in the software. Finally, we compared our approach with Ant Colony Optimization (ACO), a pheromone based optimization technique in test suite optimization and finalized that, ABC based approach has several advantages over ACO based optimization.