面向对象系统的类之间依赖关系度量研究

类之间的依赖关系，对于面向对象系统分析、设计和测试都有重要的意义。本文首先对类之间的依赖关系进行了定义和说明，并细分其为数据依赖和方法依赖，在此基础上。提出依赖度和被依赖度两种度量方法，并进行了严格的语义分析和说明。     

对象类之间依赖关系度量分析

类之间的依赖关系,对于面向对象系统分析、设计和测试都有重要的意义。该文首先对类之间的依赖关系进行了定义和说明,并细分其为数据依赖和方法依赖,在此基础上,提出依赖度和被依赖度两种度量方法,并进行了严格的语义分析和说明。最后,文章提出依据这两种度量方法来确定类的规模大小的算法。     

IRP用户视图间依赖关系的度量

用户视图间的依赖关系对于信息资源规划中的数据分析和数据建模有着重要的意义。该文对用户视图间的依赖关系进行了定义和说明，并细分其为数据依赖和操作依赖，在此基础上，对用户视图间的依赖关系进行了度量，提出了依赖度和被依赖度2种度量方法，为规范用户视图提供了依据。     

基于依赖关系的大规模主题数据库的分解模式

本文对主题数据库之间的依赖关系进行了定义和说明,并细分其为实体依赖和操作依赖.在此基础上,对主题数据库之间的依赖关系进行了度量,提出了主题数据库之间的相对依赖度和相对被依赖度,并以此确定主题数据库的规模大小.最后给出了分解主题数据库算法的描述.通过这个算法可以把大规模的主题数据库科学地分解成若干个子主题数据库,降低子主题数据库之间的依赖度,同时又保证了主题数据库内部的联系是紧密的,大大提高了检索数据库的效率,并且有利于对主题数据库的管理和维护.     

基于UML类图的类之间依赖关系图论问题研究

首先简单介绍了UML的类图，并细分类之间依赖关系为数据依赖和方法依赖，在此基础上，将UML类图转化为有向依赖图，并依据图论理论来分析和研究了有向依赖图的性质和特点，证明了有向依赖图不是自反的，也不是反自反的；既不是对称的，也不是反对称的；不是传递的。     

状态依赖的类内聚度量

类是面向对象软件中的模块,包含属性和方法。类内聚度量是对类属性和方法相关程度的度量。通过对类方法和属性的分类及其与类状态之间的关系分析,提出了类核的概念和状态依赖的类内聚度量方法。该方法有利于克服类内聚度量复杂性和判断模糊性问题,并可以在设计阶段对类的内聚进行度量。     

面向对象软件系统的一种度量方法及其应用

在面向对象系统分析和设计过程中，类的设计质量直接关系到软件系统质量。首先对类之间的关系进行了分析、说明和定义，并分为横向关系和纵向关系，给出了横向关系和纵向关系度量与质量评价，并在此基础上给出了改善类设计质量的方法。     

基于效用图的依赖关系与协商策略研究

依赖关系一直是多议题协商的重点和难点.在基于依赖关系的多议题协商背景下,对依赖关系和依赖度进行了严格的定义和度量.在协商过程中,协商Agent强化了自身的协商策略,并使用多目标遗传协商算法对协商进行优化.实验结果表明,在该协商背景下依赖关系的定义以及依赖强度的度量是合理的,卖方Agent使用多目标遗传协商算法和各种协商策略是可行的,并取得了较好的协商效果,且使协商结果迭到了pareto最优.     

时态实体依赖关系与度量方法研究

实体间存在各种各样的依赖关系,尤其是在软件开发过程中,软件实体间的依赖关系对软件的变更影响分析以及风险分析等都具有重大影响。依赖图作为最常用的依赖关系表示方法,其节点与边的定义与属性计算不尽相同,且大部分方法中并没有考虑到节点与边的时态属性。针对时态实体依赖图,文中系统地提出了时态实体依赖关系的形式化定义并分析了其特性,然后分析了时态实体依赖图的节点中心性、节点重要性、节点依赖度和边的重要性等4个度量指标,同时,针对MAVEN数据集分析了上述各个指标随时间变化的规律。     

一种UML类图结构复杂度度量方法

对类图结构复杂性进行度量可以辅助对系统概念模型进行质量评价.提出一种改进的赋权类依赖图构造方法,并提出一套基于赋权类依赖图路径分析的类图结构复杂度度量指标体系,该指标体系着眼于类间关系的整体结构.同时给出各项指标的计算方法.     

Measuring behavioral dependency for improving change-proneness prediction in UML-based design models

Several studies have explored the relationship between the metrics of the object-oriented software and the change-proneness of the classes. This knowledge can be used to help decision-making among design alternatives or assess software quality such as maintainability. Despite the increasing use of complex inheritance relationships and polymorphism in object-oriented software, there has been less emphasis on developing metrics that capture the aspect of dynamic behavior. Considering dynamic behavior metrics in conjunction with existing metrics may go a long way toward obtaining more accurate predictions of change-proneness. To address this need, we provide the behavioral dependency measure using structural and behavioral information taken from UML 2.0 design models. Model-based change-proneness prediction helps to make high-quality software by exploiting design models from the earlier phase of the software development process. The behavioral dependency measure has been evaluated on a multi-version medium size open-source project called JFlex. The results obtained show that the proposed measure is a useful indicator and can be complementary to existing object-oriented metrics for improving the accuracy of change-proneness prediction when the system contains high degree of inheritance relationships and polymorphism.     

Quality aspects for component‐based systems: A metrics based approach

In component‐based development, software systems are built by assembling components already developed and prepared for integration. To estimate the quality of components, complexity, reusability, dependability, and maintainability are the key aspects. The quality of an individual component influences the quality of the overall system. Therefore, there is a strong need to select the best quality component, both from functional and nonfunctional aspects. The present paper produces a critical analysis of metrics for various quality aspects for components and component‐based systems. These aspects include four main quality factors: complexity, dependency, reusability, and maintainability. A systematic study is applied to find as much literature as possible. A total of 49 papers were found suitable after a defined search criteria. The analysis provided in this paper has a different objective as we focused on efficiency and practical ability of the proposed approach in the selected papers. The various key attributes from these two are defined. Each paper is evaluated based on the various key parameters viz. metrics definition, implementation technique, validation, usability, data source, comparative analysis, practicability, and extendibility. The paper critically examines various quality aspects and their metrics for component‐based systems. In some papers, authors have also compared the results with other techniques. For characteristics like complexity and dependency, most of the proposed metrics are analytical. Soft computing and evolutionary approaches are either not being used or much less explored so far for these aspects, which may be the future concern for the researchers. In addition, hybrid approaches like neuro‐fuzzy, neuro‐genetic, etc., may also be examined for evaluation of these aspects. However, to conclude that one particular technique is better than others may not be appropriate. It may be true for one characteristic by considering different set of inputs and dataset but may not be true for the same with different inputs. The intension in the proposed work is to give a score for each metric proposed by the researchers based on the selected parameters, but certainly not to criticize any research contribution by authors. Copyright © 2012 John Wiley & Sons, Ltd.     

面向对象软件度量阈值的确定方法: 问题、进展与挑战

面向对象软件度量是理解和保证面向对象软件质量的重要手段之一.通过将面向对象软件的度量值与其阈值比较,可简单直观评价其是否有可能包含缺陷.确定度量阈值方法主要有基于数据分布特征的无监督学习方法和基于缺陷相关性的有监督学习方法.两类方法各有利弊:无监督学习方法无需标签信息而易于实现,但所得阈值的缺陷预测性能通常较差;有监督学习方法通过机器学习算法提升所得阈值的缺陷预测性能,但标签信息在实际过程中不易获得且度量与缺陷链接技术复杂.近年来,两类方法的研究者不断探索并取得较大进展.同时,面向对象软件度量阈值确定方法研究仍存在一些亟待解决的挑战.对近年来国内外学者在该领域的研究成果进行系统性的总结.首先,阐述面向对象软件度量阈值确定方法的研究问题.其次,分别从无监督学习方法和有监督学习方法总结相关研究进展,并梳理具体的理论和实现的技术路径.然后,简要介绍面向对象软件度量阈值的其他相关技术.最后,总结当前该领域研究过程面临的挑战并给出建议的研究方向.     

结合多元度量指标软件缺陷预测研究进展

软件缺陷预测可帮助开发人员提前预测缺陷程序,合理分配有限的测试资源。软件缺陷预测的准确度不仅依赖于预测方法的选择,更依赖于软件的度量指标。因此,结合多元度量指标进行软件缺陷预测已成为当前的研究热点。从度量指标出发,对传统度量指标、多元度量指标以及结合多元度量指标的缺陷预测的研究进展进行了系统介绍。主要工作包含：介绍了传统的代码和过程度量指标、基于传统度量指标的软件缺陷预测模型以及影响数据质量的因素;阐述了语义结构度量指标;分析列举了当前用于软件缺陷预测的评价指标;结合预测粒度、传统度量指标、语义结构度量指标、跨项目软件缺陷预测对多元度量指标软件缺陷预测未来的研究趋势进行了展望。     

模糊多值依赖的深入探讨

本文探讨了模糊多值依赖的性质，对单一模糊多值依赖环境下的推导规则进行了补充；给出了求依赖基的算法；讨论了在模糊函数依赖、模糊多值依赖混合作用下的推导公理系统；介绍了模糊嵌入多值依赖的概念及公理系统。     

Data dependency in multiple classifier systems

In this paper, the data dependency of aggregation modules in multiple classifier system is being investigated. We first propose a new categorization scheme, in which combining methods are grouped into data-independent, implicitly data-dependent and explicitly data-dependent. It is argued that data-dependent approaches present the highest potential for improved performance. In this study, we intend to provide a comprehensive investigation of this argument and explore the impact of data dependency on the performance of multiple classifiers. We evaluate this impact based on two criteria, prediction accuracy and stability. In addition, we examine the effect of class imbalance and uneven data distribution on these two criteria. This paper presents the findings of an extensive set of comparative experiments. Based on the findings, it can be concluded that data-dependent aggregation methods are generally more stable and less sensitive to class imbalance. In addition, data-dependent methods exhibited superior or identical generalization ability for most of the data sets.     

航电处理机相关性建模与故障方程生成方法

航电产品的相关性建模方法为验证故障检测/隔离能力、提高产品的测试性提供了实用方法,由相关性模型生成的故障方程能够有效地进行故障诊断.针对工程实际需求,介绍了一阶相关性模型的数据获取及模型建立方法.利用测试性建模软件建立了某型航电处理机的一阶相关性模型,并获得了相应的测试性参数及系统D矩阵.同时,探讨了由一阶相关性模型生成系统故障方程的方法,包括D矩阵压缩方法和故障方程逻辑合成两个步骤.     

面向对象程序复杂性度量层次模型

程序复杂性度量可以实现定量地分析程序复杂性，从而为估计成本提供一个标准。为了度量面向对象程序复杂性，在讨论了度量的定义．度量的理论基础后，提出了一种面向对象程序复杂性度量的层次模型。该模型分成5个层次：系统层、类簇层、类继承树层、类层和方法层，每个层次都有自己的度量方法。采用这种分层度量模型的好处是：它是一个框架，各度量方法归属到各层中；各层之间相互独立；一层中方法的修改并不影响其它层。