A weight optimized artificial neural network for automated software test oracle

Software testing has its main goal as designing new test case sets in a manner in which it is able to depict its maximum faults. As soon as these test cases have been designed, Oracle software provides a method in which the software has to behave for a particular test case given. Prioritization of such test cases with the execution of their components specifying inputs, their operation and their outcome will determine as to whether the application and their properties are working in the right manner. The prioritization methods are as follows: initial ordering, random ordering and finally reverse ordering that were based on fault detection abilities. For developing software applications, a test suite that was less commonly known as the suite for checking the validity of software was employed. The test suite contained a detailed set of instructions and goals for each test case collection based on the system and its configuration used during testing. Automating the generation of a test case and test oracle was researched in an extensive manner. From among the automated test oracle, the artificial neural network (ANN) was used extensively but with a high cost of computation. This work proposed a weight optimized ANN using stochastic diffusion search to find the optimal weights with a unique fitness function such that computational time is reduced and misclassification rate reduced.