Exploratory test oracle using multi-layer perceptron neural network

In the context of exploratory testing (ET), human knowledge and intelligence is applied as a test oracle. The exploratory tester designs and executes the tests on fly and compares the actual output produced by the application under test with the expected output in the testers' mind. The shortcoming of human oracle is that they are fallible, that is exploratory testers do not always detect a failure even when a test case reveals it. Depending on a human tester to evaluate program behaviour has also some problems such as cost and correctness. Therefore, in this paper an effort has been made to explore the feasibility of using a multilayer perceptron neural network (MLP-NN) as an exploratory test oracle. The MLP-NN was improved by adding another weight on each connection to perfectly generate reliable exploratory test oracles for transformed different data formats.