Research on Quality Prediction of Airborne Software Based on Neural Network Optimized by Ant Colony Algorithm

This study addresses the issue of software quality prediction for airborne systems and proposes an optimization method for Backpropagation Neural Network (BPNN) based on Ant Colony Algorithm, referred to as SQP-ACO-BPNN. With the advancement of neural network technology, this method aims to overcome the challenges in selecting the network structure and initial connection parameters in traditional software quality prediction models. By employing the Ant Colony Algorithm to optimize the neural network structure, initial connection weights, and thresholds, the model is capable of more accurately fitting the relationship between software features and quality. Experimental results demonstrate that SQP-ACO-BPNN significantly improves accuracy, precision, recall, and F1 score compared to the non-optimized BPNN, providing a more reliable model for software quality prediction. Future research directions include optimizing Ant Colony Algorithm parameters, incorporating other metaheuristic algorithms and deep learning techniques, adjusting neural network structures, and applying the method in different domains to advance research and practical applications in software engineering quality prediction.