Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network

This paper proposes an artificial neural network (ANN) based software reliability model trained by novel particle swarm optimization (PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.      

Nonparametric dynamically weighted combination model to determine when to stop testing

Software manufacturers need to minimize the number of their software failures in their production environments. So, software reliability becomes a critical factor for these manufacturers to focus on. Software Reliability Growth Models (SRGMs) are used as indicators of the number of failures that may be faced after the shipping of the software and thus are indicators of the readiness of the software for shipping. SRGMs to handle varying operational profiles have been proposed by researchers earlier. However, as it is difficult to predict the nature of the project in advance, the reliability engineer has to try out each model one at a time before zeroing in on the model to be used in the project. We have derived a combination model, called dynamically weighted infinite NHPP combination, using the existing models for determining the release time. The nonparametric dynamically weighted combination model that we propose was validated and was found to be effective.

     

一个基于响铃形故障检测率函数的软件可靠性增长模型

非齐次泊松过程类软件可靠性增长模型是评价软件产品可靠性指标的有效工具．影响软件可靠性增长模型评估和预测准确性的最重要的两个因素是软件中隐藏的初始故障数和故障检测率．一些非齐次泊松过程类模型假设故障检测率是不随测试时间变化的常量,有些模型假设故障检测率是增函数或减函数．这些假设或忽略了测试者的学习过程,或忽略了越迟被检测到的故障的概率就可能越低的特点．该文将测试者的学习过程和软件固有故障检测率的变化特征相结合,提出了一个铃形的故障检测率函数,建立了一个非齐次泊松过程类软件可靠性增长模型——Bbell—SRGM．在一组失效数据上的实验分析表明：对这组失效数据,Bbell—SRGM模型比G-O模型等的拟合效果更好．     

基于灰色Elman神经网络软件可靠性预测模型

针对当前软件可靠性预测模型在随机性和动态性较强的可靠性现场数据中存在预测精度波动比较大、适应性比较差的问题,提出一种基于灰色Elman神经网络的软件可靠性预测模型。首先使用灰色GM（1,1）模型对失效数据进行预测,弱化其随机性;然后采用Elman神经网络对GM（1,1）的预测残差进行建模预测,捕捉其动态性变化规律;最后将GM（1,1）预测值和Elman神经网络残差预测值相结合得到最终的预测结果。使用航班查询系统的现场失效数据集进行了模型仿真实验,并将灰色Elman神经网络预测模型与反向传播（BP）神经网络、Elman神经网络预测模型进行比较,其对应的均方误差（MSE）和平均相对误差（MRE）分别为105.1、270.9、207.5和0.0011、0.0021、0.0016,并且灰色Elman神经网络预测模型的误差均为最小值。实验结果表明该模型具有较好的预测精度。     

Enhancing and measuring the predictive capabilities of testing-effort dependent software reliability models

Software testing is necessary to accomplish highly reliable software systems. If the project manager can conduct well-planned testing activities, the consumption of related testing-resources will be cost-effective. Over the past 30 years, many software reliability growth models (SRGMs) have been proposed to estimate the reliability growth of software, and they are mostly applicable to the late stages of testing in software development. Thus far, it appears that most SRGMs do not take possible changes of testing-effort consumption rates into consideration. However, in some cases, the policies of testing-resource allocation could be changed or adjusted. Thus, in this paper, we will incorporate the important concept of multiple change-points into Weibull-type testing-effort functions. The applicability and performance of the proposed models are demonstrated through two real data sets. Experimental results show that the proposed models give a fairly accurate prediction capability. Finally, based on the proposed SRGM, constructive rules are developed for determining optimal software release times.     

RBFNN versus FFNN for daily river flow forecasting at Johor River,Malaysia

Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999–2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting.     

考虑故障相关的软件可靠性增长模型研究

软件可靠性增长模型是用来评估和预测软件可靠性的重要工具.目前,绝大多数的软件可靠性增长模型并没有考虑故障之间的相关性,也没有考虑测试环境和运行环境的区别.文中提出了一种随机过程类非齐次泊松过程(NHPP)中的考虑故障相关性、测试环境和运行环境差别的模型.在两组失效数据上的实验分析表明:对这两组失效数据,文中提出的模型比其他一些非齐次泊松过程类模型的拟合效果和预测效果更好.     

Selecting software reliability growth models and improving their predictive accuracy using historical projects data

During software development two important decisions organizations have to make are: how to allocate testing resources optimally and when the software is ready for release. SRGMs (software reliability growth models) provide empirical basis for evaluating and predicting reliability of software systems. When using SRGMs for the purpose of optimizing testing resource allocation, the model's ability to accurately predict the expected defect inflow profile is useful. For assessing release readiness, the asymptote accuracy is the most important attribute. Although more than hundred models for software reliability have been proposed and evaluated over time, there exists no clear guide on which models should be used for a given software development process or for a given industrial domain.Using defect inflow profiles from large software projects from Ericsson, Volvo Car Corporation and Saab, we evaluate commonly used SRGMs for their ability to provide empirical basis for making these decisions. We also demonstrate that using defect intensity growth rate from earlier projects increases the accuracy of the predictions. Our results show that Logistic and Gompertz models are the most accurate models; we further observe that classifying a given project based on its expected shape of defect inflow help to select the most appropriate model.     

基于CS-ANN的软件缺陷预测模型研究

为了提高软件缺陷预测的准确率,利用布谷鸟搜索算法(Cuckoo Search,CS)的寻优能力和人工神经网络算法（Artificial Neural Network,ANN）的非线性计算能力,提出了基于CS-ANN的软件缺陷预测方法。此方法首先使用基于关联规则的特征选择算法降低数据的维度,去除了噪声属性;利用布谷鸟搜索算法寻找神经网络算法的权值,然后使用权值和神经网络算法构建出预测模型;最后使用此模型完成缺陷预测。使用公开的NASA数据集进行仿真实验,结果表明该模型降低了误报率并提高了预测的准确率,综合评价指标AUC（area under the ROC curve）、F1值和G-mean都优于现有模型。     

A new methodology for optimization and prediction of rate of penetration during drilling operations

Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is artificial neural network (ANN). ANN models in combination with optimization algorithms provide a powerful and fast tool for the prediction and optimization of processes which take a large amount of time if they are simulated using common simulation technics. In the present paper, to predict penetration rate during drilling process, several ANN models were developed based on the data obtained from drilling of a gas well located in south of Iran. Regarding the R2 and RMSE values of the developed models, the best model was selected for prediction of penetration rate. In the next step, artificial bee colony algorithm was used for optimization of the parameters which are effective on rate of penetration (ROP). Results showed that the model is accurate enough for being used in the prediction and optimization of ROP in drilling operations.     

An Empirical Method for Selecting Software Reliability Growth Models

Estimating remaining defects (or failures) in software can help test managers make release decisions during testing. Several methods exist to estimate defect content, among them a variety of software reliability growth models (SRGMs). SRGMs have underlying assumptions that are often violated in practice, but empirical evidence has shown that many are quite robust despite these assumption violations. The problem is that, because of assumption violations, it is often difficult to know which models to apply in practice. We present an empirical method for selecting SRGMs to make release decisions. The method provides guidelines on how to select among the SRGMs to decide on the best model to use as failures are reported during the test phase. The method applies various SRGMs iteratively during system test. They are fitted to weekly cumulative failure data and used to estimate the expected remaining number of failures in software after release. If the SRGMs pass proposed criteria, they may then be used to make release decisions. The method is applied in a case study using defect reports from system testing of three releases of a large medical record system to determine how well it predicts the expected total number of failures.     

基于学习向量量化神经网络的软件可靠性预测

针对传统的软件可靠性预测模型在实际应用中存在预测泛化性能不佳等问题,提出一种基于学习向量量化（LVQ）神经网络的软件可靠性预测模型。首先分析了LVQ神经网络的结构特点以及它与软件可靠性预测的联系,然后运用该网络来进行软件可靠性的预测,并基于美国国家航空航天局（NASA）软件数据项目中的实例数据集,运用Matlab工具进行了仿真实验。通过与传统预测方法的对比,证明该方法具有可行性和较高的预测泛化性能。     

软件可靠性及可靠性多模型综合研究

软件可靠性验证阶段的可靠性增长模型的建立与选择是软件可靠性工程人员长期关注的焦点。首先对软件可靠性基本概念及影响因素和工程确认的几种软件可靠性增长模型进行阐述,探讨近年来工程人员对软件可靠性增长模型的改进方法研究进展,提出了基于模型聚类的混合模型方法,并对该方法进行了实验性仿真分析。     

基于改进型神经网络的软件可靠性模型

针对传统软件可靠性模型需要分析软件体系结构的可行性问题,提出使用改进型神经网络计算可靠性的模型。采用自组织算法,优化设计隐含层神经网络,并使用测试数据训练网络,得到可靠性模型。实验结果证明,该模型能够在缺乏对软件内部结构分析的情况下作出与传统模型同样精确的预测。     

基于进化神经网络对软件可靠性的预测

用神经网络对软件可靠性进行预测比传统的NHPP预测模型预测精度高、拟合度好。但是神经网络的结构是有经验的神经网络专家根据实际经验得来的。而该文提出了用遗传算法去优化神经网络的结构,很好的解决了神经网络的结构问题。用进化的神经网络对软件可靠性的预测提高了预测的精度和准确度。     

基于进化神经网络对软件可靠性的预测

用神经网络对软件可靠性进行预测比传统的NHPP预测模型预测精度高、拟合度好。但是神经网络的结构是有经验的神经网络专家根据实际经验得来的。而该文提出了用遗传算法去优化神经网络的结构,很好的解决了神经网络的结构问题。用进化的神经网络对软件可靠性的预测提高了预测的精度和准确度。     

Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir

In this work we investigate how artificial neural network (ANN) evolution with genetic algorithm (GA) improves the reliability and predictability of artificial neural network. This strategy is applied to predict permeability of Mansuri Bangestan reservoir located in Ahwaz, Iran utilizing available geophysical well log data. Our methodology utilizes a hybrid genetic algorithm–neural network strategy (GA–ANN). The proposed algorithm combines the local searching ability of the gradient–based back-propagation (BP) strategy with the global searching ability of genetic algorithms. Genetic algorithms are used to decide the initial weights of the gradient decent methods so that all the initial weights can be searched intelligently. The genetic operators and parameters are carefully designed and set avoiding premature convergence and permutation problems. For an evaluation purpose, the performance and generalization capabilities of GA–ANN are compared with those of models developed with the common technique of BP. The results demonstrate that carefully designed genetic algorithm-based neural network outperforms the gradient descent-based neural network.     

Prediction of quality using ANN based on Teaching‐Learning Optimization in component‐based software systems

The primary objective of our research work is to enhance the prediction of the quality of a component‐based software system and to develop an artificial neural network (ANN) model for the system reliability optimization problem. In this paper, we introduced the ANN‐supported Teaching‐Learning Optimization by transforming constraints to objective functions. Artificial neural network techniques are found to be powerful in the modeling software package quality metrics compared with the ancient statistical techniques. Therefore, by using the neural network, the quality characteristics of software components of the proposed work are predicted. A nonlinear differentiable transfer function of ANN used in the proposed approach is hyperbolic tangent sigmoid. A new efficient optimization methodology referred to as the Teaching‐Learning–based Optimization is proposed in this paper to optimize reliability and different cost functions. The weight values of the network are then adjusted consistent with a proposed optimization rule, therefore minimizing the network error. The proposed work is implemented in MATLAB by using the Neural Network Toolbox. The proposed work provides improved performance in terms of sensitivity, precision, specificity, negative predictive value, fall‐out or false positive rate, false discovery rate, accuracy, Matthews correlation coefficient, and rate of convergence.     

A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA–ANN model for the prediction of time series data. Many of the hybrid ARIMA–ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a moving-average filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA–ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA–ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy.     

灰色-神经网络综合预测模型

该文提出一种灰色-神经网络综合预测模型。该模型由背景值构造、加权GM(1,1)模型和神经网络补偿器三部分组成。其建模机理为:首先对于原始数列进行背景值构造,然后构建加权GM(1,1)模型,同时利用神经网络补偿器获得误差补偿信号,则最终的预测值为加权GM模型的输出值加上补偿值。仿真结果验证了所提方法的有效性。