Abstract
A more automated graphic user interface (GUI) test model, which is based on the event-flow graph, is proposed. In the model, a user interface automation API tool is first used to carry out reverse engineering for a GUI test sample so as to obtain the event-flow graph. Then two approaches are adopted to create GUI test sample cases. That is to say, an improved ant colony optimization (ACO) algorithm is employed to establish a sequence of testing cases in the course of the daily smoke test. The sequence goes through all object event points in the event-flow graph. On the other hand, the spanning tree obtained by deep breadth-first search (BFS) approach is utilized to obtain the testing cases from goal point to outset point in the course of the deep regression test. Finally, these cases are applied to test the new GUI. Moreover, according to the above-mentioned model, a corresponding prototype system based on Microsoft UI automation framework is developed, thus giving a more effective way to improve the GUI automation test in Windows OS.
Highlights
Testing graphic user interface (GUI) is a hard and monotonous labor
Aiming at the requirements of GUI automation test, an improved ant colony optimization algorithm is employed to establish a sequence of testing cases in the course of the daily smoke test
The algorithm based on the spanning tree obtained by deep breadth-first search (BFS) approach for the deep regression test is described as follows
Summary
Testing GUI is a hard and monotonous labor. So far, a large number of scholars and experts have been addressing themselves to the study of related fields. After having come to recognize the fact that it increasingly did not satisfy the modeling requirements of GUI automation test, experts proposed an event-flow model based on event-flow graph They investigated a variety of automatic generation approaches to GUI test cases, which were closely connected with the adopted GUI model like above-mentioned CIS. The spanning tree obtained by deep BFS approach is utilized to obtain the testing cases from goal point to outset point in the course of the deep regression test These cases are applied to test the new GUI.
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