基于模糊综合评价的软件测试性度量

考虑软件测试性和测试性特性的模糊性,提出一个基于模糊综合评价的软件测试性度量方法。方法分为模糊度量测试性特性和模糊度量软件测试性两个阶段,每个阶段都基本遵循经典的模糊综合评价方法,分为建立因素集、确定评价集、单因素评价、确定权重和综合评价。为了准确度量软件测试性,方法将测试性特性度量得到的模糊综合评价值作为软件测试性度量的输入。方法不仅能判断软件测试性和测试性特性等级,还能计算它们的具体数值,很好地削弱了模糊性对软件测试性度量的影响。     

基于LDRA Testbed的民用机载软件静态测试方法

静态测试作为软件测试的重要方法,是保证民用机载软件中安全关键软件质量的关键步骤。介绍静态测试的概念和方法,采用自动化分析方法,基于软件分析工具LDRA Testbed从主要静态分析、复杂度分析、静态数据流、交叉索引、信息流和数据对象分析六大部分完成软件的静态测试过程,探究其测试原理和关键标准文件的配置,生成相应的代码审查和测试度量报告,以有效提高民用机载软件质量。     

软件测试质量的度量

该文在详细分析了软件测试充分性和揭错能力度量理论后,根据软件质量的本质特性,提出一种更全面的软件测试质量度量方法———通过度量软件测试方法减低软件残留风险程度的能力来度量测试质量的新方法。     

基于基准比对思想的软件可靠性预测仿真建模研究

可靠性作为衡量软件质量的重要特性,其定量评估和预测已成为人们关注和研究的焦点。本文针对这个问题展开研究,提出一个可用于软件测试之前的早期可靠性预测仿真模型。此仿真模型通过考查影响软件可靠性的过程因素,采用基准比对思想,利用软件过程度量数据,根据相似度比较,预测软件的残留缺陷数。由于该仿真模型仅需要静态历史数据,故可在软件测试之前,用于估计软件的残留缺陷数,从而预测软件的可靠性,为后期软件过程的改进以及软件测试计划的修正提供依据。     

软件故障静态预测方法综述

软件故障静态预测通过从项目数据中提取度量信息预测故障,以便于测试和验证资源的分配。从可用度量数据和预测模型两个方面总结了软件故障静态预测方法,可用度量包括方法层、类层、构件层、文件层以及过程层度量,预测模型分为机器学习和统计方法两类;总结了性能评价指标、度量数据可得性以及故障分类对故障预测的影响等需要进一步研究的问题。     

软件测试性度量框架研究

针对当前软件测试性度量框架适用范围有限、无法扩展的问题,提出一个新的度量框架。分析软件测试性概念,使用层次分析法得到新的框架结构。总结框架扩展方法,将测试性特性和影响因素加入到框架以进行软件测试性度量。其稳定性保证了框架对任何软件都具有相同的测试性度量过程和计算方法,可扩展性使得框架能随着软件技术的发展不断完善,两者为框架的通用性提供了保证。     

基于模糊推理的软件测试度量方法

如何形成软件测试有效的成本效益策略一直以来都是软件工程的重要研究内容.许多研究者致力于软件测试的有效性和质量度量的研究并已取得大量成果.但是,软件测试中最重要的问题之一--测试度量中内在的不确定和不相关的关系仍然没有解决.由此,提出一种新的基于模糊逻辑的软件测试质量和测试有效性度量方法,探讨了基于软件质量特性和相似性推理的方法,试图解决不同测试工作任务之间的测试质量和有效性一致的问题.同时提供实验的结果证明提出的方法的可行性.     

浅议软件测试

介绍了软件测试的必要性和目的,阐述了软件测试的静态测试、动态测试和黑、白盒测试法,以及软件测试的一般过程和步骤,及软件测试的几个原则。     

面向对象软件的测试评估

软件测试的目的是发现错误,而不是确认其正确性,是为了增强人们对软件能够按照需求者的期望正确运行的信心,因此需要对软件测试质量进行度量.由于面向对象软件所具有的特性,面向对象软件的测试的度量相对于在过程测试中的测试度量已经不再适应.利用切片和领域的概念,通过多层次的测试度量来对面向对象软件的测试进行评估,该方法能够为改进对面向对象软件系统的测试过程和测试策略提供帮助.     

基于过程度量的软件测试质量管理

软件测试验证软件是否符合用户需求,软件测试过程的科学管理是软件测试成功的重要保证,运用软件度量的方法量化软件测试过程,构造度量并分析过程度量数据的有效性;论述测试效率/软件缺陷作为软件测试工作量/成果指标的必要性,运用工作量/成果模型分析测试过程;通过量化软件相关属性实现过程度量的构造,实施度量后利用工作量/成果模型对度量结果进行分析,并对项目做出评估,并采取相应措施;在现有资源的情况下,实施质量管理,监控软件测试过程,实现软件测试过程的有效管理。     

基于Bayes网的时间序列预测

时间序列预测问题在气象、天文、电力、医学、生物、经济、金融和计算机等各个领域有着广泛的应用。本文将Bayes网模型用于该领域，提出并建立了一套基于Bayes的时间序列预测模型——静态]3ayes网预测模型，动态Bayes网预测模型和分类静态Bayes网预测模型。实验表明，这三个模型能更准确地描述用户在Web上的浏览特征，在预测准确率和存储复杂度方面都显著地优于传统的时间序列预测模型。     

Forecasting volatility for the stock market: a new hybrid model

This study presents a new hybrid model that combines the grey forecasting model with the GARCH to improve the variance forecasting ability in variance as compared to the traditional GARCH. A range-based measure of ex post volatility is employed as a proxy for the unobservable volatility process in evaluating the forecasting ability due to true underlying volatility process not being observed. Overall, the results show that the new hybrid model can enhance the volatility forecasting ability of the traditional GARCH.     

Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model

Several studies have applied genetic programming (GP) to the task of forecasting with favorable results. However, these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new ldquodynamicrdquo GP model that is specifically tailored for forecasting in nonstatic environments. This dynamic forecasting genetic program (DyFor GP) model incorporates features that allow it to adapt to changing environments automatically as well as retain knowledge learned from previously encountered environments. The DyFor GP model is tested for forecasting efficacy on both simulated and actual time series including the U.S. Gross Domestic Product and Consumer Price Index Inflation. Results show that the performance of the DyFor GP model improves upon that of benchmark models for all experiments. These findings highlight the DyFor GP's potential as an adaptive, nonlinear model for real-world forecasting applications and suggest further investigations.     

碰撞检测技术综述

综述了当前出现的各种碰撞检测算法。总体上讲大部分的算法都采用先采样然后进行静态碰撞检测的方法。不同算法采样的方式不同决定了算法的种类,距离预测法或者是时间预测法。减少采样次数,提高静态检测的速度是降低算法复杂度最重要的因素。目前研究的重点有层次数据表示法、动态距离跟踪算法、静态检测算法等。     

一种获取渔场知识的数据挖掘模型及知识表示方法研究*

提出了一种获取渔场知识的数据挖掘模型及知识表示方法。首先利用SVM(支持向量机)和模糊分类器从数据库中提取出有利于渔情预测的静态知识,然后通过可拓挖掘方法将静态知识转换为动态知识,最后采用本体构建技术对渔场的静态知识和动态知识进行表达,建立本体知识库。在上述方法的研究基础上,建立了以印度洋大眼金枪鱼为例的渔情预测原型系统。系统运行结果表明,提出的获取渔场知识的数据挖掘模型及知识表示方法是有效可行的。     

一种基于fMRI数据的脑功能网络构建方法*

人脑可以用复杂网络方法进行定量分析。为了研究基于功能磁共振成像数据来构建脑功能网络,首先,用标准脑模板将全脑分割成90个功能区域,每个区域定义为一个网络节点;然后,用脑区的平均时间序列来计算相关系数, 网络节点间是否有边相连取决于其相关水平;最后,生成一系列不同网络密度的无向无权图,用来分析网络统计特性。结果表明,所构建的网络具有小世界拓扑结构。该脑功能网络的构建方法可以应用在某些认知障碍的临床诊断上。     

交叉熵算法在企业违约风险评估中的应用研究

系统仿真是风险评价的一种重要手段,针对企业违约预测问题,提出了一种基于交叉熵算法的违约风险评判方法。采用公司未偿还贷款的概率作为衡量违约风险高低的标准,利用交叉熵方法构造企业违约风险识别模型及其算法,并由此估计出发生损失的概率。与传统的预测方法进行比较,结果表明该模型对违约风险具有很强的识别能力,预测精度高。     

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.     

Applying Dynamic Training‐Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility

Volatility is a key variable in option pricing, trading, and hedging strategies. The purpose of this article is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training‐subset selection methods. These methods manipulate the training data in order to improve the out‐of‐sample patterns fitting. When applied with the static subset selection method using a single training data sample, GP could generate forecasting models, which are not adapted to some out‐of‐sample fitness cases. In order to improve the predictive accuracy of generated GP patterns, dynamic subset selection methods are introduced to the GP algorithm allowing a regular change of the training sample during evolution. Four dynamic training‐subset selection methods are proposed based on random, sequential, or adaptive subset selection. The latest approach uses an adaptive subset weight measuring the sample difficulty according to the fitness cases' errors. Using real data from S&P500 index options, these techniques are compared with the static subset selection method. Based on mean squared error total and percentage of non‐fitted observations, results show that the dynamic approach improves the forecasting performance of the generated GP models, especially those obtained from the adaptive‐random training‐subset selection method applied to the whole set of training samples.     

A model updating strategy for predicting time series with seasonal patterns

Traditional methodologies for time series prediction take the series to be predicted and split it into training, validation, and test sets. The first one serves to construct forecasting models, the second set for model selection, and the third one is used to evaluate the final model. Different time series approaches such as ARIMA and exponential smoothing, as well as regression techniques such as neural networks and support vector regression, have been successfully used to develop forecasting models. A problem that has not yet received proper attention, however, is how to update such forecasting models when new data arrives, i.e. when a new event of the considered time series occurs.This paper presents a strategy to update support vector regression based forecasting models for time series with seasonal patterns. The basic idea of this updating strategy is to add the most recent data to the training set every time a predefined number of observations takes place. This way, information in new data is taken into account in model construction. The proposed strategy outperforms the respective static version in almost all time series studied in this work, considering three different error measures.