关键词: 软件质量预测, 蚁群算法, 神经网络, 网络结构评价, 反向传播

Abstract: In order to overcome the defaults of the model including slow convergence and low accuracy in software quality prediction method based on Back Propagation (BP) neural network, a Software Quality Prediction method based on Ant Colony Optimization (ACO) improved BP Neural Network (SQP-ACO-BPNN) was proposed to determine the best network structure, initial connection weights and thresholds of the network before training the model in the proposed method. Firstly, the software quality evaluation factors were selected and a software quality evaluation system was considered. Secondly, BP neural network was adopted to build software quality prediction model and ACO was used to determine network structures, initial connection weights and thresholds of network. Then, an evaluation function  was given to select the best structure, initial connection weights and thresholds of the network. Finally, the network  was trained by BP algorithm, and the software quality prediction model was obtained. The experimental results of predicting the quality of airborne embedded software showthat the accuracy, precision, recall and F1 value of theoptimized neural network model with faster convergence are all improved, which indicate the validity of the proposed method.

Key words: software quality prediction, Ant Colony Algorithm (ACO), neural network, network structure evaluation, Back Propagation (BP)