Abstract
This research addresses the difficulties of underfitting, overfitting, and convergence to local minima in artificial neural networks for software dependability prediction. The work specifically focuses on enhancing the performance of the conventional PSO-SVM model for software reliability prediction. The analysis of the conventional PSO-SVM model and the special features of software reliability prediction serve as the foundation. An improved PSO-SVM software reliability prediction model is developed and the PSO-SVM model and a Backpropagation (BP) prediction model are compared experimentally. The critical metrics assessed include training error, and efficiency. The experimental results reveal that the training error of the enhanced PSO-LSSVM prediction model diminishes rapidly, levelling off after approximately 200 training generations. The BP prediction model requires 1,733 generations to meet training requirements. Furthermore, the improved PSO-LSSVM prediction model demonstrates significantly higher training efficiency than the BP prediction model. The optimized prediction model exhibits superior adaptability to small sample sizes, swift training, and high prediction accuracy, making it a more suitable choice for software reliability prediction applications.
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