The performance of the Software defect prediction is significantly inclined by the characteristics of the software metrics which raises the efficacy of the software substantially. During the development and maintenance phase, it is very expensive to detect and rectify software module defects. This research work aims at developing an intelligent software prediction model to predict the defect in software system in an efficient manner. The dataset used in this research work is collected from NASA repository. Since the dataset is voluminous it is handled by reducing its feature subset with the use of greedy stepwise search algorithm. This algorithm selects the most prominent features which are influence the dependent variable to predict the defect and defect free modules. After feature reduction the prediction process is done by evolutionary artificial neural network. The standard artificial neural network is enhanced by applying genetic algorithm to improvise the performance of the learning phase.
The genetic algorithm fine tunes the parameters applied in hidden nodes of the hidden layer. By assigning the weights and bias with the knowledge acquired from genetic algorithm the performance of the artificial neural network in the software defect prediction produces more accurate results. This is proved by performing simulation on the proposed evolutionary artificial neural network (MGNN) using MATLAB simulator. From the result it is proved that the performance of the MGNN is better while comparing with Standard Artificial Neural Network (ANN), Probabilistic Neural Network (PNN) and Group Method of Data Handling (GMDH)
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Genetic Neural Network, Software Defect, ANN, PNN, GMDH.