基于蚁群算法优化反向传播神经网络的软件质量预测  

Software quality prediction based on back propagation neural network optimized by ant colony optimization algorithm

作　　者：朱嘉豪 郑巍[1,2] 杨丰玉[1,2] 樊鑫[1,2] 肖鹏 ZHU Jiahao;ZHENG Wei;YANG Fengyu;FAN Xin;XIAO Peng(School of Software,Nanchang Hangkong University,Jiangxi Nanchang 330063,China;Software Testing and Evaluation Center,Nanchang Hangkong university,Jiangxi Nanchang 330063,China)

机构地区：[1]南昌航空大学软件学院,南昌330063 [2]南昌航空大学软件测评中心,南昌330063

出　　处：《计算机应用》2023年第11期3568-3573,共6页journal of Computer Applications

基　　金：总装预研基金项目(JZX7J202202ZL002000)。

摘　　要：针对基于反向传播神经网络(BPNN)的软件质量预测模型存在收敛慢、模型精度不高的问题,提出一种基于蚁群算法优化BPNN的软件质量预测(SQP-ACO-BPNN)方法。首先,选择软件质量评价指标,确立软件质量评价体系;其次,采用BPNN构建初始软件质量预测模型,并利用蚁群优化(ACO)算法确定若干网络结构、网络初始连接权值和阈值;再次,给出网络结构评价函数,选择神经网络模型的最佳结构、网络初始连接权值和阈值;最后,通过BP算法训练该网络,得到最终的软件质量预测模型。在机载嵌入式软件质量预测数据上的实验结果表明,优化后的BPNN模型有效提高了预测的准确率、精确率、召回率和F1值,并且模型能够更快收敛,验证了SQP-ACO-BPNN方法的有效性。Concerning the problems of slow convergence and low accuracy of software quality prediction model based on Back Propagation Neural Network(BPNN),a Software Quality Prediction method based on BPNN optimized by Ant Colony Optimization algorithm(SQP-ACO-BPNN)was proposed.Firstly,the software quality evaluation factors were selected and a software quality evaluation system was determined.Secondly,BPNN was adopted to build initial software quality prediction model and ACO algorithm 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 final software quality prediction model was obtained.Experimental results of predicting the quality of airborne embedded software show that the accuracy,precision,recall and F1 value of the optimized BPNN model are all improved with faster convergence,which indicates the validity of SQP-ACOBPNN.

关 键 词：软件质量预测 蚁群优化算法 反向传播神经网络 网络结构评价 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论] TP18[自动化与计算机技术—计算机科学与技术]