Application of improved cuckoo algorithm to optimize generalized regression neural network in software quality prediction

Abstract

Software quality prediction technology is the main method of early prediction and control of software quality. Generalized regression neural network (GRNN) can better map the nonlinear relationship between software metrics and software quality elements, but the prediction accuracy of the software quality prediction model based on GRNN is low. To improve the accuracy of the quality prediction model, we use the improved cuckoo search (CS) algorithm to optimize the smoothing factor of GRNN, solve the problems of insufficient population diversity and slow convergence speed in the later stage of the cuckoo algorithm, and propose a software quality prediction model based on the improved CS algorithm to optimize GRNN by introducing Gaussian disturbance function, to improve the accuracy of predicting the number of software defects. Finally, the paper uses the public promise data set for simulation experiments and verifies the model by comparing it with the GRNN model optimized by the CS algorithm and the standard GRNN model.