基于排错等待延迟的广义动态集成神经网络模型  

A generalized dynamic integrated neural network model based on fault-correction waiting delay

作　　者：惠子青 刘晓燕[1] 严馨[1] HUI Zi-qing;LIU Xiao-yan;YAN Xin(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650500

出　　处：《计算机工程与科学》2020年第4期641-648,共8页Computer Engineering & Science

基　　金：国家自然科学基金(61462055)。

摘　　要：软件可靠性增长模型在可靠性评估与保障中具有重要作用,针对软件测试过程中的故障检测和排错等待延迟问题,提出了一种考虑故障排错等待延迟的广义动态集成神经网络模型(RWD-SRGM)。该模型考虑软件工程的多样性,利用神经网络方法构建广义动态集成模型,并考虑排错等待延迟现象完成故障检测和预测。通过2组真实失效数据集(DS1和DS2)的实验,将所提模型与现有的软件可靠性增长模型进行了比较,结果显示考虑故障排错等待延迟的神经网络模型拟合效果最优,表现出了更好的软件可靠性评估性能和模型通用性。The software reliability growth model plays an important role in reliability evaluation and guarantee. Aiming at the problems of fault detection and fault-correction waiting delay in software testing, this paper proposes a generalized dynamic integrated neural network model considering the fault-correction waiting delay. The model considers the diversity of software engineering. It uses the neural network method to construct a generalized dynamic integration model, and considers the fault-correction waiting delay phenomenon to complete the fault detection and prediction. Through the experiments on two real failure datasets(DS1 and DS2), the proposed method is compared with the existing software reliability growth model. The results show that the neural network model considering the fault-correction waiting delay has the best fitting effect, and exhibits better software reliability assessment performance and model versatility.

关 键 词：软件可靠性 软件可靠性增长模型 排错等待延迟 广义动态集成网络 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]