Neural Networks Based Test Cases Selection Strategy for GUI Testing*

The purpose of graphical user interfaces (GUI) testing is to diagnose and expose faults in planning time. It is difficult because the input space of GUI is extremely large due to different permutations of inputs and events. To test GUI needs to run a lot of test cases. Neural networks (NN) were explored to reduce test cases to expose new faults. The main idea is as follows. Firstly, NN was trained by subset of test cases that had executed and their test results. Trained NN could recognize fault patterns that had been exposed. Secondly, from the test suite that hadn´t been executed, trained NN was used to select test cases that don´t belong to the fault patterns. The test cases selected were more likely to expose new faults in GUI. By the method new faults could be exposed by executing fewer test cases. The experimental results show that the strategy is effective