功能点分析方法的一种形式化定义

针对功能点分析(FPA)方法因缺少精确化定义而导致度量结果与实际之间有一定偏差的问题,基于B方法对FPA的度量规则进行形式化定义,即为功能点计算提供一个明确的定义。实例应用表明,把B方法应用到软件度量中,能够提高软件项目管理的效率,为软件功能规模的自动化度量奠定基础。     

功能点的计算方法

目前,软件复杂度越来越高,有的甚至会成为管理系统的系统,虽然这些软件有着不同的目标,但最终它们都必须相互协调的在一起共同发展。然而,随着软件的复杂度的上升,软件的可控性却在下降。由此,软件度量在软件开发过程中的地位日益增强。因为只有做好软件度量工作,才能在很短的时间内,做出正确的决策,保证产品的质量,才能在日益激烈的竞争中取得胜利。而软件度量的方法有很多,例如FPA、LOC等等,本文主要关注的功能点分析方法,主要是探讨一下FPA中的功能点的计算方法。     

功能点的计算方法

目前，软件复杂度越来越高，有的甚至会成为管理系统的系统，虽然这些软件有着不同的目标，但最终它们都必须相互协调的在一起共同发展。然而，随着软件的复杂度的上升，软件的可控性却在下降。由此，软件度量在软件开发过程中的地位日益增强。因为只有做好软件度量工作，才能在很短的时间内，做出正确的决策，保证产品的质量，才能在日益激烈的竞争中取得胜利。而软件度量的方法有很多．例如FPA、LOC等等．本文主要关注的功能点分析方法，主要是探讨一下FPA中的功能点的计算方法。     

面向对象方法的功能点度量

功能点分析（FPA）本身只能度量系统静态的方面,不能考虑到对象的交互性和对象的行为。基于FPA,结合面向对象系统的特点,提出了一种面向对象方法的功能点,完成了从FPA方法到面向对象方法概念上的映射和度量规则上的映射,并给出了该方法具体的度量过程,为Web应用的度量奠定了坚实的理论基础。     

一种改进的功能点分析方法

功能点分析是一种广泛使用的软件功能规模度量方法，它不依赖于实现语言，度量结果也可以在不同的开发过程之间进行比较。该文针对功能点分析方法中功能要素复杂度等级划分的缺点和不足，提出了模糊功能点分析方法。该方法能更准确地估算出功能点数量，具有很强的实际可操作性。     

COSMIC-FFP方法的研究及应用

随着我国软件外包行业的日渐兴起,越来越多公司开始注重对软件开发过程的控制,对软件度量的投入也越来越多.阐述了一种基于传统的IFPUG-FPA模型的演进规模估计模型-COSMIC-FFP模型及其应用.并提出了一种基于COSMIC全功能点和ISO 9126的新的定量的度量技术.应用COSMIC全功能点的规则和原理的软件结构度量了基本的交互变量,如耦合、内聚和复杂度.在ISO9126质量标准的基础上,将设计的度量方法应用于度量质量属性,如可维护性、可用性和可信度.     

一种通用的软件功能规模度量模型

功能规模度量（FSM）方法通过量化用户功能需求(FUR)而得到软件功能规模。针对不同的功能规模度量方法都是使用不同的抽象来描述一个软件系统的问题,提出了一种通用的FSM模型。根据软件系统的抽象模型,首先对度量所涉及的数据组和事务进行了泛化,然后以IFPUG FPA为例详细说明了该通用模型和FPA之间的转换过程,最后给出了度量过程的算法描述。     

基于Java程序的功能点度量

针对软件项目开发中维护阶段的成本和工作量难以估计的问题,提出一种基于Java程序的功能点度量方法,并介绍从目标程序中抽取数据功能点以及事务功能点的计算规则,同时以Java源代码为例说明具体的度量过程。实验结果表明,该方法是有效可行的。     

基于UML建模技术的功能点度量研究

软件工作量估算是软件项目管理的重要组成部分之一,功能点度量方法逐渐成为该领域的主要方法.本文从UML部件(用例图、类图和顺序图)中识别出功能点度量方法中的事务功能和数据功能,分析其复杂度,最终得出功能点数,进而将功能点度量方法与UML建模技术相结合,实现了UML部件(用例图、类图和顺序图)向功能点的映射,并结合IFPUG的功能点估算步骤,提出了基于UML建模技术的功能点分析步骤.实证结果表明该方法可以进一步精化基于UML建模软件项目的工作量度量结果,便于项目管理人员控制软件项目活动,合理安排人员等资源,可以在一定程度上解决软件项目频繁超支和超时的问题.     

区间值加权模糊推理方法

提出了一种新的带权的区间值模糊产生式规则,给出了加权模糊匹配函数和一个区间值排序算法。在此基础上给出了区间值加权模糊推理方法。该方法使规则前件与事实的匹配更符合实际情况,得到的结果更便于实际应用,并且解决了文献犤1,2犦中模糊推理方法存在的不足。     

扩展的用例点估算方法

通过分析用例点估算技术的不足之处,提出了两个扩展方法:一是使用模糊集理论扩展用例复杂度权重因子表,使权重因子能够更准确地反应事务数目的变化,从而使估算的结果更准确、可信;二是采用分解的方法使用例点方法能够实现对单个用例的估算,为项目计划提供必要的数据支持。扩展方法简明实用、灵活性强,易于软件组织实施和推广。     

Application of genetic algorithm for fuzzy rules optimization on semi expert judgment automation using Pittsburg approach

Genetic algorithm is well-known of its best heuristic search method. Fuzzy logic unveils the advantage of interpretability. Genetic fuzzy system exploits potential of optimization with ease of understanding that facilitates rules optimization. This paper presents the optimization of fourteen fuzzy rules for semi expert judgment automation of early activity based duration estimation in software project management. The goal of the optimization is to reduce linguistic terms complexity and improve estimation accuracy of the fuzzy rule set while at the same time maintaining a similar degree of interpretability. The optimized numbers of linguistic terms in fuzzy rules by 27.76% using simplistic binary encoding mechanism managed to improve accuracy by 14.29% and reduce optimization execution time by 6.95% without compromising on interpretability in addition to promote improvement of knowledge base in fuzzy rule based systems.     

一种软件项目的风险评估模型及应用

文章提出了一种软件项目风险评估模型。采用AHP方法与模糊逻辑法相结合的方法进行风险评估,并根据软件项目管理的实际情况对AHP方法与模糊逻辑法进行了改造。最后确定出软件项目各风险的权重。在确定了项目的风险,并做出了评估和排序后,对这些风险采取适当的方法进行有效的风险控制。通过实例分析可知,利用该方法可以方便地求出软件项目风险权重,实验结果符合实际。     

课堂教学质量的计算机模糊评估系统的研究

应用模糊数学的相关理论,结合课堂教学质量评估指标体系,提出一种课堂教学质量的计算机模糊评估系统的设计方案,采用了多策略指标权重确定方法和模糊数学技术对课堂教学质量进行综合评估,以期对课堂教学质量的评估更加科学、客观、公正、准确。     

基于改进AHP-熵博弈赋权的输变电工程评价

针对现有单一赋权法在输变电工程评价过程中存在的片面性问题,为了提高评价结果的科学性与准确性,提出了一种基于博弈论组合赋权的模糊综合评价方法;在综合考虑输变电工程项目特点的基础上,依据项目全寿命周期理论建立综合评价指标体系;通过博弈论赋权的方法,组合改进AHP(层次分析法)求取的主观权重与熵值法求取的客观权重,得出一种均衡主客观结果的最优权重;结合模糊数学理论,建立模糊综合评价模型,并以该模型对湖南长沙地区某220 kV输变电工程进行评价,验证了该方法的科学性与可行性.     

The application of fuzzy logic to the precautionary principle

One of the major problems in the implementation of the precautionary principle in environmental cases is the estimation of the weight of evidence. In this paper we propose a formal method that determines the weight of evidence based on the specific parameters of a given case. The proposed method is based on an artificial intelligence approach called fuzzy logic, which is commonly used as an interface between logic and human perception, and often applied to computer-based complex decision making. We use one fuzzy expert system that provides a quantification of the estimated environmental damage, and a second fuzzy expert system that computes the weight of evidence in a given case. The proposed expert system can be easily defined and adjusted by regulators and environmental science and policy experts.     

Neuro-f uzzy system modeling based on automatic f uzzy clustering

A neuro-fuzzy system model based on automatic fuzzy clustering is proposed. A hybrid model identification algorithm is also developed to decide the model structure and model parameters. The algorithm mainly includes three parts :1) Automatic fuzzy C-means (AFCM) , which is applied to generate fuzzy rules automatically , and then fix on the size of the neuro-fuzzy network , by which the complexity of system design is reducesd greatly at the price of the fitting capability; 2)Recursive least square estimation ( RLSE) . It is used to update the parameters of Takagi-Sugeno model , which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network. Finally ,modeling the dynamical equation of the two- link manipulator with the proposed approach is illustrated to validate the feasibility of the method.     

Neuro-fuzzy system modeling based on automatic fuzzy clustering

A neuro-fuzzy system model based on automatic fuzzy dustering is proposed. A hybrid model identification algorithm is also developed to decide the model structure and model parameters. The algorithm mainly includes three parts：1） Automatic fuzzy C-means （AFCM）, which is applied to generate fuzzy rttles automatically, and then fix on the size of the neuro-fuzzy network, by which the complexity of system design is reducesd greatly at the price of the fitting capability; 2） R.ecursive least square estimation （RLSE）. It is used to update the parameters of Takagi-Sugeno model, which is employed to describe the behavior of the system;3） Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network. Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.     

项目组合交互风险的网络聚类及协同演化研究

将交互关系纳入项目组合风险研究框架,分析项目组合交互风险网络复杂性及其演化规律。从交互关系分类入手,将项目组合表征为由多个子系统相互作用构成的交互耦合网络,并提出了基于直觉模糊生态聚类算法的项目组合交互风险网络聚类识别模型;基于扩展的Lotka-Volterra方程建立项目组合交互风险协同演化模型,对比研究具有竞争、共生和偏利偏害效应的项目组合交互风险演化规律。研究表明,项目组合风险效率最大化及系统稳定性的关键在于项目组合交互关系及其强度,同时合理控制组合配置比例。     

Type-2 fuzzy modeling for desulphurization of steel process

This paper presents a new type-2 fuzzy logic system model for desulphurization process of a real steel industry in Canada. The type-2 fuzzy logic system permits us to model rule uncertainties where every membership value of an element has a second order membership value of its own. In this paper, we propose an indirect method to create second order membership grades that are amplitudes of type-2 secondary membership functions, where the primary memberships are extracted by implementation of fuzzy clustering approach. In this research, Gaussian Mixture Model (GMM) is used for the creation of second order membership grades. Furthermore, a reduction scheme is implemented which results in type-1 membership grades. In turn, this leads to a reduction of the complexity of the system. Two methods are used for the estimation of the membership functions: indirect and direct methods. In the indirect method, the system uses an interpolation scheme for the estimation of the most appropriate membership functions. In the direct method, the system is tuned by an inference algorithm for the optimization of the main parametric system. In this case, the parameters are: Schweizer and Sklar t-norm and s-norm, combination of FATI and FITA inference approaches, and Yager defuzzification. Finally, the system model is applied to the desulphurization process of a real steel industry in Canada. It is shown that the proposed type-2 fuzzy logic system is superior in comparison to multiple regression and type-1 fuzzy logic systems in terms of robustness, and error reduction.