NEURAL NETWORK OPTIMIZATION USING SHUFFLEDFROG ALGORITHM FOR SOFTWARE DEFECT PREDICTION

Software Defect Prediction (SDP) focuses on the detection of system modules such as files, methods, classes, components and so on which could potentially consist of a great amount of errors. SDP models refer to those that attempt to anticipate possible defects through test data. A relation is present among software metrics and the error disposition of the software. To resolve issues of classification, for the past many years, Neural Networks (NN) have been in use. The efficacy of such networks rely on the pattern of hidden layers as well as in the computation of the weights which link various nodes. Structural optimization is performed in order to increase the quality of the network frameworks, in two separate cases: The first is the typically utilized approximation error for the present data, and the second is the capacity of the network to absorb various issues of a general class of issues in a rapid manner along with excellent precision. The notion of Back Propagation (BP) is quite elementary; the result of neural networks is tested against the desired outcome. Genetic algorithms (GA) are a type of search algorithms built, based on the idea of natural evolution. A neural network using Shuffled Frog Algorithm for improving SDP is proposed.