基于结合自适应步长布谷鸟搜查算法的模糊神经网络的软件可靠性增长模型

doi:10.11772/j.issn.1001-9081.2014.10.2908

计算机应用 ›› 2014, Vol. 34 ›› Issue (10): 2908-2912.DOI: 10.11772/j.issn.1001-9081.2014.10.2908

基于结合自适应步长布谷鸟搜查算法的模糊神经网络的软件可靠性增长模型

刘逻,郭立红

中国科学院长春光学精密机械与物理研究所，长春 130033

收稿日期:2014-04-14 修回日期:2014-06-13 发布日期:2014-10-30 出版日期:2014-10-01
通讯作者: 刘逻
作者简介:刘逻（1983-），男，四川成都人，博士，主要研究方向：软件可靠性、软件测试；
郭立红（1964-），女，吉林舒兰人，研究员，博士，主要研究方向：激光对抗、形体自动化标校。
基金资助:
国家863计划项目

Software reliability growth model based on self-adaptive step cuckoo search algorithm fuzzy neural network

LIU Luo,GUO Lihong

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun Jilin 130033, China

Received:2014-04-14 Revised:2014-06-13 Online:2014-10-30 Published:2014-10-01
Contact: LIU Luo

摘要/Abstract

摘要：

针对现有的软件可靠性增长模型(SRGM)适用性较差、预测精度波动大的问题,使用自适应步长布谷鸟搜查(ASCS)算法对模糊神经网络（FNN）的权重和阈值进行寻优，利用得到了最优权重和阈值的FNN建立SRGM。在使用缺陷数据对FNN训练的过程中，利用ASCS来调整FNN的权重和阈值，以此提高在预测过程中的精度，同时采用多次预测结果取均值的方式来减小FNN预测的波动性,以此建立基于结合自适应步长布谷鸟搜查算法的模糊神经网络（ASCS-FNN）的软件可靠性增长模型。利用3组软件缺陷数据，以误差比均值和误差平方和作为衡量标准，对基于ASCS-FNN、结合模拟退火算法的动态模糊神经网络（SA-DFNN）、FNN、BP网络（BPN）建立的SRGM的一步向前预测能力进行比较。预测结果表明，在四组模型中，基于ASCS-FNN建立的SRGM相对于SA-DFNN、FNN、BPN建立的SRGM的平均预测精度相对提高率RI(AE)和RI(SSE)分别为-1.48%、54.8%、33.8%和14.4%、76%、35.9%，并且该模型比FNN、BPN建立的SRGM在相同缺陷数据下的预测波动性小，而且网络结构比SA-DFNN的网络结构简单。因此该模型具有预测精度较高、预测稳定和具有一定的适用性等优点。

Abstract: According to the poor applicability and poor prediction accuracy fluctuation of the existing Software Reliability Growth Model (SRGM), this paper proposed a model based on Fuzzy Neural Network (FNN) which was connected with self-Adaptive Step Cuckoo Search (ASCS) algorithm, the weights and thresholds of the FNN were optimized by ASCS algorithm, then the FNN was used to establish SRGM. Software defect data were used in the FNNs training process, the weights and thresholds of FNN were adjusted by ASCS, the accuracy of prediction process was improved correspondingly, at the same time, in order to reduce the fluctuation of prediction by FNN, averaging method was used to deal with predicted results. Based on those, SRGM was established by self-Adaptive Step Cuckoo Search algorithm—Fuzzy Neural Network (ASCS-FNN). According to 3 groups of software defect data, taking Average Error (AE) and Sum of Squared Error (SSE) as measurements, the SRGMs one-step forward predictive ability established by ASCS-FNN was compared with the SRGMs one-step forward predictive ability established by Simulated Annealing—Dynamic Fuzzy Neural Network (SA-DFNN), FNN and Back Propagation Network (BPN). The simulation results confirm that, the SRGM based on ASCS-FNN relative to the SRGM based on SA-DFNN, FNN and BPN, the mean of Relative Increase (RI) of prediction accuracy rate for RI (AE) is -1.48%, 54.8%, 33.8%, and the mean of Relative Increase (RI) of prediction accuracy rate for RI (SSE) is 14.4%, 76%, 35.9%. The prediction of SRGM established by ASCS-FNN is more steadily than the prediction of SRGM established by FNN and BPN, and the net structure of ASCS-FNN is much simpler than the net structure of SA-DFNN, so the SRGM established by ASCS-FNN has high prediction accuracy, prediction stability, and some adaptability.

中图分类号:

TP181

刘逻郭立红. 基于结合自适应步长布谷鸟搜查算法的模糊神经网络的软件可靠性增长模型[J]. 计算机应用, 2014, 34(10): 2908-2912.

LIU Luo GUO Lihong. Software reliability growth model based on self-adaptive step cuckoo search algorithm fuzzy neural network[J]. Journal of Computer Applications, 2014, 34(10): 2908-2912.

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基于结合自适应步长布谷鸟搜查算法的模糊神经网络的软件可靠性增长模型

Software reliability growth model based on self-adaptive step cuckoo search algorithm fuzzy neural network

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