Dynamically Testing GUIs Using Ant Colony Optimization (T)

In this paper we introduce a dynamic GUI test generator that incorporates ant colony optimization. We created two ant systems for generating tests. Our first ant system implements the normal ant colony optimization algorithm in order to traverse the GUI and find good event sequences. Our second ant system, called AntQ, implements the antq algorithm that incorporates Q-Learning, which is a behavioral reinforcement learning technique. Both systems use the same fitness function in order to determine good paths through the GUI. Our fitness function looks at the amount of change in the GUI state that each event causes. Events that have a larger impact on the GUI state will be favored in future tests. We compared our two ant systems to random selection. We ran experiments on six subject applications and report on the code coverage and fault finding abilities of all three algorithms.