Fully Automated Gui Testing and Coverage Analysis Using Genetic Algorithms

Graphical user interface (GUI), is a graphical front-end to a software system, contains graphical objects with certain distinct values whose purpose is to ascertain the state of the GUI at any time. In order to ensure that the quality of the software is par excellence, software developing organizations endeavor to test the software meticulously. Nevertheless, the process of testing a GUI application calls for a colossal effort, owing on account of the intricacy entailed in such applications. Subsequently, organizations were spurred to initiate the automation of GUI testing, thereby proposing various techniques to achieve this end. A GUI model event-flow graph, an innovative technique being utilized in the field of automated GUI testing, represents, likewise control flow graph, all promising progressions of events that can be executed on GUI. The search for utmost quality insurance for software, through the introduction of automated software testing, raises yet another challenging question, that of the “amount” of testing required so as procuring the best results. In the course of the development of the techniques for the automation of the software testing procedure, a few measures can be employed to provide guidance on the quality of an automatic test suite. Based on some predefined test criterion, genetic algorithm searches for the best possible test parameter combinations. Usually, this test criterion corresponds to a “coverage function” that measures how much of the automatically generated optimization parameters satisfies the given test criterion. In this paper, we have attempted to exploit the event driven nature of GUI. Based on this nature, we have presented a GUI testing and coverage analysis technique centered on genetic algorithms.