软件科学中Halstead模型的改进

针对Halstead软件复杂性度量模型中存在的缺陷，提出三种修改方案：加权模型、DC模型和OOM模型。对Halstead模型的修改，对于软件可靠性工程和软件的过程控制工程均有重要的现实意义。另外模型的提出也为进一步研制面向对象软件复杂性度量工具提供了理论依据。     

基于依赖矩阵的构件软件复杂性的度量模型

目前的构件软件复杂性度量模型未考虑构件之间不同依赖关系和软件构件内部复杂性两个重要因素,度量结果不够完整、准确.针对该问题,通过将软件体系结构抽象为加权的有向图,获得构件之间的依赖矩阵和影响矩阵,进而获取复杂性的度量公式.从度量公式分析和最后的示例可以得出,该度量模型可以更加真实、准确地反映构件之间不同的依赖关系和构件内部复杂性对软件复杂性的影响,而且具有简单、易于实现等特点.     

混源软件质量模型与度量方法研究

由于混源软件包含自主代码、开源代码等不同来源代码,从而具有更高的多样性和复杂性,对其质量的度量评估与传统软件存在极大区别。为了度量混源软件质量,建立混源软件质量度量模型和方法是非常必要的。通过分析混源软件质量特性,提出混源软件质量模型。然后利用层次分析法、幂性法及线性法构建度量方法体系。最后对UbuntuKylin操作系统进行了实验性的度量评估,验证了模型与方法的可行性和有效性。     

一种基于PSM的CMMI过程度量模型

软件过程技术为开发人员提供一个标准的软件开发规范，使得软件开发生产率得以提高。在众多过程技术中SEI推出的软件能力成熟度集成模型(CMMI)过程评估改进框架被广泛地接受和使用。该文阐述了CMMI评估度量框架在实施时所存在的不足，提出了一种针对CMMI并结合PSM理论的软件过程定义度量模型PSM4CMMI，分析了该模型各个组件的作用，介绍了如何利用该模型实现SPMAS系统。     

路径复杂性度量研究

软件复杂性度量是对程序静态特性和动态行为的理解难易程度的描述。本文通过分析传统的程序复杂性度量方法的不足之处，提出了一种新的路径复杂性度量方法及计算路径复杂度的算法，并给出了实例。新的度量方法比传统的度量方法更精确和容易实现。     

软件复杂性度量系统的研制

结合软件复杂性度量的各种算法 ,对我们自行研制开发的一种软件复杂性度量系统 (SCES)进行了详细介绍 ,并将该系统与已有的各种度量工具进行了分析比较。     

构件软件的回归测试复杂性度量

基于构件的软件构建方法目前被广泛使用在软件开发中,用于减少软件开发的工程成本和加快软件开发进度.在软件维护过程中,由于构件更新或者新版本的发布,基于构件的系统会受到影响,需要进行回归测试.对于指定的软件修改需求,维护者可以实施不同的修改手段.不同的修改手段会导致不同的回归测试复杂性,这种复杂性是软件维护成本和有效性的重要因素.目前的研究没有强调构件软件的回归测试复杂性问题.基于修改影响复杂性模型和度量,提出一种回归测试的复杂性度量框架.该度量框架包括两个部分:基于图的模型和形式化度量计算.该度量可以有效表示构件软件分别在构件和系统层面的回归测试复杂性因素,可视化地体现复杂性变化.然后根据模型,提出具体的度量计算方式.最后,通过实验研究,针对同一个构件软件的相同修改需求,利用若干个实验组进行独立修改实施,然后比较回归测试的复杂性.实验结果表明,所提出的度量方式是可行和有效的.     

基于McCabe的软件复杂性度量与控制策略

软件复杂性度量与控制是软件开发面临的主要问题。本文通过对软件复杂性的定量分析，提出了软件复杂性的控制策略，该策略对开发高质量、高可靠性与高可雏护性软件有一定的指导作用。     

基于GQM模型的软件项目进度的度量过程

把QGM(Quality Goal Metric)模型引入到软件项目进度的度量过程中，使得软件过程的进度具有可控性、可测性。介绍了GQM模型，并且基于一个实际的甘特图实例，给出了详细的GQM度量计划、度量构造，最后得出相应的度量指示器，分析、总结度量结果。     

一种软件复杂性度量系统的设计与实现

本文结合软件复杂性度量的多种算法,对我们研制开发的一个软件复杂性度量系统,进行了详细介绍,并将系统与已有的各种度量工具进行了分析比较。     

基于Bayes网的软件失效预测模型

Owing to the complexity of software development,the software reliability model should not only have the capability of dealing with multiple complex factors,but also provide the furtction of flexibility in construction.So far,no software reliability model is universally applicable.The main reason for this is of too many conditions ,thus making software reliability models introvert.Bayesian network is a powerful tool for solving this problem,which exhibits strong adaptability in dealing with problems involving complex variant factors.In the paper,software failure predication model based oft Markov.Bayesian network is established and analyzed thoroughly.Then a method of solving the model is given.Finally,through an example the validity of the model is validated.     

基于多重马尔可夫Bayes网的软件失效预测模型

软件开发的复杂性决定了理想的软件可靠性模型既应具有包容众多复杂因素的能力,又要有构造灵活的功能。迄今为止,人们提出的众多模型,由于设定了很多近乎苛刻的条件,使它们难以具有普适性。Bayes网提供了解决这一问题的有力工具,论文就利用多重马尔可夫Bayes网建立起基于Bayes网的软件失效预测模型,并对此进行了详细的分析,给出了模型的求解步骤。最后,通过实例验证了该模型的有效性。     

A probabilistic model for predicting software development effort

Recently, Bayesian probabilistic models have been used for predicting software development effort. One of the reasons for the interest in the use of Bayesian probabilistic models, when compared to traditional point forecast estimation models, is that Bayesian models provide tools for risk estimation and allow decision-makers to combine historical data with subjective expert estimates. In this paper, we use a Bayesian network model and illustrate how a belief updating procedure can be used to incorporate decision-making risks. We develop a causal model from the literature and, using a data set of 33 real-world software projects, we illustrate how decision-making risks can be incorporated in the Bayesian networks. We compare the predictive performance of the Bayesian model with popular nonparametric neural-network and regression tree forecasting models and show that the Bayesian model is a competitive model for forecasting software development effort.     

面向广义质量体系的软件质量贝叶斯评估模型

现有软件质量评估模型主要关注软件系统的基本质量特性,缺乏考虑顾客价值特性和开发商组织管理特性,不能全面和科学地评估软件系统质量.文章以贝叶斯网络刻画了广义质量特性变量间复杂依赖关系,构建了更有针对性的软件质量量化评估模型.应用实例表明,该模型能综合考虑软件系统广义质量特性,对软件质量作出合理评价,并能依据贝叶斯网络的反向推理功能找到影响软件质量的关键因素.     

Software effort estimation based on the optimal Bayesian belief network

In this paper, we present a model for software effort (person-month) estimation based on three levels Bayesian network and 15 components of COCOMO and software size. The Bayesian network works with discrete intervals for nodes. However, we consider the intervals of all nodes of network as fuzzy numbers. Also, we obtain the optimal updating coefficient of effort estimation based on the concept of optimal control using Genetic algorithm and Particle swarm optimization for the COCOMO NASA database. In the other words, estimated value of effort is modified by determining the optimal coefficient. Also, we estimate the software effort with considering software quality in terms of the number of defects which is detected and removed in three steps of requirements specification, design and coding. If the number of defects is more than the specified threshold then the model is returned to the current step and an additional effort is added to the estimated effort. The results of model indicate that optimal updating coefficient obtained by genetic algorithm increases the accuracy of estimation significantly. Also, results of comparing the proposed model with the other ones indicate that the accuracy of the model is more than the other models.     

On the use of Bayesian belief networks for the prediction of software productivity

In spite of numerous methods proposed, software cost estimation remains an open issue and in most situations expert judgment is still being used. In this paper, we propose the use of Bayesian belief networks (BBNs), already applied in other software engineering areas, to support expert judgment in software cost estimation. We briefly present BBNs and their advantages for expert opinion support and we propose their use for productivity estimation. We illustrate our approach by giving two examples, one based on the COCOMO81 cost factors and a second one, dealing with productivity in ERP system localization.     

Range estimation of construction costs using neural networks with bootstrap prediction intervals

Modeling of construction costs is a challenging task, as it requires representation of complex relations between factors and project costs with sparse and noisy data. In this paper, neural networks with bootstrap prediction intervals are presented for range estimation of construction costs. In the integrated approach, neural networks are used for modeling the mapping function between the factors and costs, and bootstrap method is used to quantify the level of variability included in the estimated costs. The integrated method is applied to range estimation of building projects. Two techniques; elimination of the input variables, and Bayesian regularization were implemented to improve generalization capabilities of the neural network models. The proposed modeling approach enables identification of parsimonious mapping function between the factors and cost and, provides a tool to quantify the prediction variability of the neural network models. Hence, the integrated approach presents a robust and pragmatic alternative for conceptual estimation of costs.     

基于贝叶斯网络的风力发电机故障诊断方法

贝叶斯网络是概率统计学的重要分支,具有强大的不确定性问题处理能力,适用于复杂系统的故障诊断。风力发电机系统维护成本较高,为减少维修成本,需要进行准确的故障定位;文章对基于贝叶斯网络的故障诊断方法进行了研究,介绍了贝叶斯网络故障诊断模型的建立过程,并着重介绍了诊断算法推导和计算过程;利用历史故障统计数据建立了风力发电机系统贝叶斯网络Matlab模型,主要包括网络结构有向无环图和条件概率分布参数等内容;最后,模拟了两种故障,分别采用贝叶斯网络方法和相关性矩阵方法进行故障诊断,通过对两种方法诊断结果的比较,前者具有更好的故障分辨率,可有力支持复杂系统的维护保障、降低维修成本。     

A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping

The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty.     

Variational Bayesian methods for spatial data analysis

With scientific data available at geocoded locations, investigators are increasingly turning to spatial process models for carrying out statistical inference. However, fitting spatial models often involves expensive matrix decompositions, whose computational complexity increases in cubic order with the number of spatial locations. This situation is aggravated in Bayesian settings where such computations are required once at every iteration of the Markov chain Monte Carlo (MCMC) algorithms. In this paper, we describe the use of Variational Bayesian (VB) methods as an alternative to MCMC to approximate the posterior distributions of complex spatial models. Variational methods, which have been used extensively in Bayesian machine learning for several years, provide a lower bound on the marginal likelihood, which can be computed efficiently. We provide results for the variational updates in several models especially emphasizing their use in multivariate spatial analysis. We demonstrate estimation and model comparisons from VB methods by using simulated data as well as environmental data sets and compare them with inference from MCMC.