ABSTRACT In this paper we present a new, non-pheromone-based test suite optimization approach inspired by the behavior of biological bees. Our proposed approach is based on ABC (Artificial Bee Colony Optimization) which is motivated by the intelligent behavior of honey bees. In our proposed system, the sites are the nodes in the Software under Test (SUT), the artificial bees modify the test cases with time and the bee’s aim is to discover the places of nodes with higher coverage and finally the one with the highest usage by the given test case. Since ABC system combines local search methods carried out by employed bees with global search methods managed by onlookers and scouts, we attain near global optima. We investigate whether this new approach outperforms existing test optimization approach based on Genetic Algorithms (GA) in the task of software test optimization. Taking into account the results of our experiments, we conclude that (i) the proposed approach uses fewer iterations to complete the task; (ii) is more scalable, i.e., it requires less computation time to complete the task, and finally (iii) our approach is best in achieving near global optimal solution.
