Using genetic algorithms for test case generation and selection optimization

Genetic Algorithms (GAs) are adaptive search techniques that imitate the processes of evolution to solve optimization problems when traditional methods are considered too costly in terms of processing time and output effectiveness. In This research, we will use the concept of genetic algorithms to optimize the generation of test cases from the application user interfaces. This is accomplished through encoding the location of each control in the GUI graph to be uniquely represented and forming the GUI controls' graph. After generating a test case, the binary sequence of its controls is saved to be compared with future sequences. This is implemented to ensure that the algorithm will generate a unique test case or path through the GUI flow graph every time.