Robust Fault-Tolerant Training Strategy Using Neural Network to Perform Functional Testing of Software

This paper is intended to introduce an efficient as well as robust training mechanism for a neural network which can be used for testing the functionality of software. The traditional setup of neural network architecture is used constituting the two phases -training phase and evaluation phase. The input test cases are to be trained in first phase and consequently they behave like normal test cases to predict the output as untrained test cases. The test oracle measures the deviation between the outputs of untrained test cases with trained test cases and authorizes a final decision. Our framework can be applied to systems where number of test cases outnumbers the functionalities or the system under test is too complex. It can also be applied to the test case development when the modules of a system become tedious after modification.