一种优化的面向对象软件复杂性度量方法

首先结合面向对象技术特性,对面向对象软件类级别的CK度量方法和系统级别的MOOD度量方法进行了分析,并就CK度量提出了优化的度量方法OCK。然后结合OCK度量和MOOD度量的优点提出了较优化的面向对象软件复杂性度量方法OSCM。OSCM度量可以有效地弥补CK度量和MOOD度量的不足,优化度量结果。     

Coad&Yourdon模型下CK度量扩展方法的研究

在对Coad&Yourdon模型和CK度量进行分析的基础上,提出对Coad&Yourdon模型主题层结构进行细化的原则以及相应的扩展CK度量模型.探讨了将CK度量方法用于对Coad&Yourdon模型度量的过程与方法,使CK度量的适用范围扩充至面向对象分析(OOA)阶段.最后给出相关实例进行了具体的说明.     

Coad ＆ Yourdon模型下CK度量扩展方法的研究

在对Coad＆Yourdon模型和CK度量进行分析的基础上，提出对Coad＆Yourdon模型主题层结构进行细化的原则以及相应的扩展CK度量模型。探讨了将CK度量方法用于对Coad＆Yourdon模型度量的过程与方法，使CK度量的适用范围扩充至面向对象分析（00A）阶段。最后给出相关实例进行了具体的说明。     

面向对象软件度量的统计分析研究

本文介绍了传统度量方法和CK度量方法的几种常用度量指标在面向对象软件度量中的具体应用,并通过统计分析说明各种指标在面向对象系统设计、测试、维护和重用之间的相互影响关系,为开发高质量面向对象软件提供实践指导.     

青鸟构件库的构件度量

对软件产业发展所需要的软件生产率和软件质量的重视引起了对软件复用技术的研究,同时,有关复用度量的研究和应用也引起了广泛的重视.青鸟构件库系统可以对可复用构件进行描述、管理、存储和检索,用以满足基于“构件-构架”复用的软件开发过程的需要.该文介绍了青鸟构件库中的构件度量模型,并阐述了利用青鸟程序分析系统中的面向对象度量工具和青鸟构件库后期度量系统(包括反馈信息的收集、处理和分析工具)来实现模型的方法.     

面向Agent的软件度量研究

本文提出了一套面向Agent软件度量的新指标：知识量、学习能力、反应时间、Agent总数、依赖度、被依赖度、交互数、总交互数、社会理性和智商。并在此基础上开发了一个度量工具原型：度量AgentMA。     

面向对象软件扇入耦合度量研究

针对C＆K度量组中耦合度量指标（CBO）没有区分扇入耦合和扇出耦合对软件质量影响的情况，提出了一组正交的对象间扇入耦合度量组，并对该度量组中各度量指标对软件质量的指示作用进行了讨论。在Frank Sauer开发的基于Eclipse度量计算插件的基础上实现了该度量组中各度量指标的自动计算。最后，通过对JUnit3．8．1和JUnit7．0扇入耦合度量计算结果的分析，验证了该文提出的扇入耦合度量组的有效性。     

一种软件质量的模糊度量函数生成方法研究

根据软件质量的属性,给出了软件质量的模糊度量与模糊度量函数的概念,以及以软件测试结果为基础的生成法,通过多元模糊度量函数、复合模糊度量函数来建立软件质量的模糊度量模型.本文的方法能很好地适用于多因素、关系复杂及对质量影响具有不确定性的情况,是对软件质量更科学、更客观的一种度量方法.     

基于语料库的潜语义信息度量

为关键词定义了与主题或语义相关联的信息度量.首先获取基于主题的语料库,然后建立语料库的潜语义向量空间模型,通过该模型定义关键词的信息度量.由此可以计算任意文档包含该主题的信息量,定义文档对主题的隶属度.设定文档对主题隶属度阈值,从而判断文档是否属于该主题类.实验表明,与主题或语义关联的信息度量可以克服搜索中"词匹配"的不足,达到"语义匹配"的搜索.     

面向对象软件度量技术

提出了OO度量评价规则和标识一组OO度量套件的方法，一组切实可行的OO度量套件及其形式化描述方法，评价OO关键概念和结构特性。质量度量模型是软件质量评价体系中的关键技术，采用层次结构的度量模型。通过质量度量模型，就建立了软件内部属性和外部属性间的客观的定量的联系。OO软件度量框架用以规范化地帮助评测软件产品质量的过程。     

Object-oriented product metrics: A generic framework

In spite of considerable prior research, a generic framework has not emerged for structuring work on object-oriented (OO) metrics. We propose such a framework (Generic Framework) for object-oriented product metrics. The framework captures the generic structure of the underlying metrics space (Metrics Space) based on a mereological and set theoretic perspective of the building blocks of OO systems and relational measurement theory. We validate the framework by applying it to a repository of about 350 product metrics. The validation shows that the framework does, indeed, capture the underlying metrics space, and can be useful in identifying gaps and additional metrics that can extend the manner in which Metrics Space is currently populated.     

Prediction of Run-Time Failures Using Static Product Quality Metrics

A study is presented in which it is determined whether software product metrics gathered statically from designs or source code may be helpful in predicting the number of run-time faults that will be encountered during execution. Metrics examined include intermodule metrics such as fan-in and fan-out, as well as intramodule metrics such as cyclomatic complexity and size. Our study indicates that it may be possible, with certain classes of software products, to predict the run-time behaviour using well-known static intermodule metrics.     

面向对象度量—MOOD算法集及其应用分析

面向对象软件的主要特性包括抽象性、封装性、继承性和多态性等。本文在分析这些特性的基础上,介绍了一种能对这些特性进行全面度量的算法集,并对这种算法集在实际应用中可能出现的问题进行了分析,提出了一些新的观点。     

基于面向对象系统的质量度量模型PMMSQ的设计和实现

本文首先从软件质量度量的角度出发,阐述了软件质量度量的理论与方法,介绍了ISO9126模型;针对已有软件质量度量模型中存在的问题,提出了一个适合于软件质量度量实践的实用软件质量度量模型（PMMSQ）,并详细介绍了面向对象系统软件质量度量的步骤;最后,根据PMMSQ模型完成了软件度量活动的核心工作--对象特征集存储库的设
计和实现。     

Techniques of Successful Application of Factor Analysis in Software Measurement

Metrics in a software sense can be roughly divided into two groups, internal metrics and external metrics. Internal metrics can be measured directly, while external metrics are predicted using internal metrics. Factor analysis is a statistical technique which can be applied to analyze the structure of internal metrics and form a prediction model for external metrics A prediction model generated by using factor analysis needs further validation since factor analysis has a non-unique solution for the factor-loading matrix. The computational methodology to establish a model is presented as well as the validation process. This is illustrated by an empirical study of design metrics and a prediction model for design quality.     

网络抗毁性测度研究

针对网络抗毁性测度问题,依据抗毁性三原则——阻挡、识别和恢复,提出了多维网络抗毁性测度提取方法,该方法综合运用了多种抗毁性分析技术,结合实际验证筛选,为提炼抗毁性测度提供了有效途径;提出了三层树状结构的网络抗毁性测度集,为抗毁性测度提供了可行的分类参考;最后,提出了抗毁性测度度量方法并给出了其形式化描述,实现不同类型抗毁性测度值的获取。分析表明,提出的网络抗毁性测度提取方法、测度集合和度量方法具有良好的一致性和可行性。     

An Empirical Exploration of the Distributions of the Chidamber and Kemerer Object-Oriented Metrics Suite

The object-oriented metrics suite proposed by Chidamber and Kemerer (CK) is a measurement approach towards improved object-oriented design and development practices. However, existing studies evidence traces of collinearity between some of the metrics and low ranges of other metrics, two facts which may endanger the validity of models based on the CK suite. As high correlation may be an indicator of collinearity, in this paper, we empirically determine to what extent high correlations and low ranges might be expected among CK metrics.To draw as much general conclusions as possible, we extract the CK metrics from a large data set (200 public domain projects) and we apply statistical meta-analysis techniques to strengthen the validity of our results. Homogenously through the projects, we found a moderate (0.50) to high correlation (>0.80) between some of the metrics and low ranges of other metrics.Results of this empirical analysis supply researchers and practitioners with three main advises: a) to avoid the use in prediction systems of CK metrics that have correlation more than 0.80 b) to test for collinearity those metrics that present moderate correlations (between 0.50 and 0.60) c) to avoid the use as response in continuous parametric regression analysis of the metrics presenting low variance. This might therefore suggest that a prediction system may not be based on the whole CK metrics suite, but only on a subset consisting of those metrics that do not present either high correlation or low ranges.     

Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes

Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their practical use in industrial settings. In this paper, we empirically validate three OO metrics suites for their ability to predict software quality in terms of fault-proneness: the Chidamber and Kemerer (CK) metrics, Abreu's Metrics for Object-Oriented Design (MOOD), and Bansiya and Davis' Quality Metrics for Object-Oriented Design (QMOOD). Some CK class metrics have previously been shown to be good predictors of initial OO software quality. However, the other two suites have not been heavily validated except by their original proposers. Here, we explore the ability of these three metrics suites to predict fault-prone classes using defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. We conclude that the CK and QMOOD suites contain similar components and produce statistical models that are effective in detecting error-prone classes. We also conclude that the class components in the MOOD metrics suite are not good class fault-proneness predictors. Analyzing multivariate binary logistic regression models across six Rhino versions indicates these models may be useful in assessing quality in OO classes produced using modern highly iterative or agile software development processes.