软件质量模型的研究

如何提供高质量的软件已成为计算机软件领域中的重要研究课题。为了理解、度量以及预测软件和信息系统的质量，人们围绕软件质量模型展开了很多研究。一个合适的软件质量模型对软件系统的开发与评价将起到关键作用。本文介绍了软件质量与软件质量度量的基本概念，重点分析了软件质量模型方面的研究成果。     

基于遗传算法的软件质量预测

研究提高软件质量问题,软件质量是一种智力产品,质量度量属性较多,传统神经网络无法准确提取最优度量软件质量属性,预测准确率低。为了提高软件质量预测准确率,将遗传算法引入到软件质量度量属性选择中。首先采用遗传算法选择最优软件质量度量属性,然后将度量属性输入神经网络进行训练,建立软件质量预测模型。通过仿真对模型性能进行测试,结果表明,遗传神经网络对软件质量预测模型降低软件质量预测错误率,提高预测准确率,在理论和实际上都具有创新性。     

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.     

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。     

软件构件质量度量

基于软件质量标准ISO／IEC9126：2001《软件工程产品质量》，探讨了软件构件的质量度量问题，首次提出了完整的软件构件质量模型：外部和内部质量模型以及使用质量模型，为度量软件构件质量提供了新的方法。最后以外部易组装性度量为例，给出了软件构件质量度量的量化方法。     

基于源代码的软件可维护性质量度量方法

随着科学技术的飞速发展,计算机技术应用越来越广,软件在各个领域都起到了至关重要的作用.软件质量问题是科学技术发展的关键因素,保证软件质量在软件开发过程中应始终保持高标准.对软件的可维护性,在软件开发初期极易被忽视,导致软件开发质量不高,软件维护成本居高不下.基于《GB/T 16260软件工程-产品质量》国家推荐标准,以软件的可维护性作为切入点,结合工作中的实际案例,介绍了基于可维护性的软件质量度量方法,并从源代码的角度对软件的可维护性进行度量和评估.     

软件质量度量研究分析

随着计算机技术的飞速发展,对软件质量的要求也更高了,软件质量度量就是衡量软件品质的一种手段。本文分析了软件质量度量模型,建立了软件质量度量框架,并给出了常用度量方法。     

软件质量度量研究分析

随着计算机技术的飞速发展,对软件质量的要求也更高了,软件质量度量就是衡量软件品质的一种手段。本文分析了软件质量度量模型,建立了软件质量度量框架,并给出了常用度量方法。     

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。     

Software cost estimation: Present and future

The state-of-the-art in software cost estimation is reviewed. The estimated cost of a software system varies widely with the model used. Some variation in cost estimation is attributable to the anomolies in the cost data base used in developing the model. The other variations, it is claimed are due to the presence or absence of certain ‘qualities’ in the final product. These qualities are measures of ‘goodness’ in design, development, and test-integration phases of software. To consider quality as a driver of software cost, we have suggested an association between cost and quality and have proposed a way to use quality metrics to estimate software cost.     

Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization

The identification of a module's fault-proneness is very important for minimizing cost and improving the effectiveness of the software development process. How to obtain the correlation between software metrics and module's fault-proneness has been the focus of much research. This paper presents the application of hybrid artificial neural network (ANN) and Quantum Particle Swarm Optimization (QPSO) in software fault-proneness prediction. ANN is used for classifying software modules into fault-proneness or non fault-proneness categories, and QPSO is applied for reducing dimensionality. The experiment results show that the proposed prediction approach can establish the correlation between software metrics and modules’ fault-proneness, and is very simple because its implementation requires neither extra cost nor expert's knowledge. Proposed prediction approach can provide the potential software modules with fault-proneness to software developers, so developers only need to focus on these software modules, which may minimize effort and cost of software maintenance.     

Applying machine learning to software fault-proneness prediction

The importance of software testing to quality assurance cannot be overemphasized. The estimation of a module’s fault-proneness is important for minimizing cost and improving the effectiveness of the software testing process. Unfortunately, no general technique for estimating software fault-proneness is available. The observed correlation between some software metrics and fault-proneness has resulted in a variety of predictive models based on multiple metrics. Much work has concentrated on how to select the software metrics that are most likely to indicate fault-proneness. In this paper, we propose the use of machine learning for this purpose. Specifically, given historical data on software metric values and number of reported errors, an Artificial Neural Network (ANN) is trained. Then, in order to determine the importance of each software metric in predicting fault-proneness, a sensitivity analysis is performed on the trained ANN. The software metrics that are deemed to be the most critical are then used as the basis of an ANN-based predictive model of a continuous measure of fault-proneness. We also view fault-proneness prediction as a binary classification task (i.e., a module can either contain errors or be error-free) and use Support Vector Machines (SVM) as a state-of-the-art classification method. We perform a comparative experimental study of the effectiveness of ANNs and SVMs on a data set obtained from NASA’s Metrics Data Program data repository.     

On the usefulness of ownership metrics in open-source software projects

ContextCode ownership metrics were recently defined in order to distinguish major and minor contributors of a software module, and to assess whether the ownership of such a module is strong or shared between developers.ObjectiveThe relationship between these metrics and software quality was initially validated on proprietary software projects. Our objective in this paper is to evaluate such relationship in open-source software projects, and to compare these metrics to other code and process metrics.MethodOn a newly crafted dataset of seven open-source software projects, we perform, using inferential statistics, an analysis of code ownership metrics and their relationship with software quality.ResultsWe confirm the existence of a relationship between code ownership and software quality, but the relative importance of ownership metrics in multiple linear regression models is low compared to metrics such as the number of lines of code, the number of modifications performed over the last release, or the number of developers of a module.ConclusionAlthough we do find a relationship between code ownership and software quality, the added value of ownership metrics compared to other metrics is still to be proven.     

面向对象软件度量结果的分析方法与评价

软件度量是保证软件质量的重要方法，该文主要针对面向对象软件经过分析度量后得出结果进行分析与评价，分析与评价主要基于面向对象程序中类的耦合度、内聚度、继承性、复杂度等进行展开，从数学的角度来分析度量指标的可信度，以保证从整体上把握面向对象软件产品的质量。     

Learning from optimization: A case study with Apache Ant

ContextSoftware architecture degrades when changes violating the design-time architectural intents are imposed on the software throughout its life cycle. Such phenomenon is called architecture erosion. When changes are not controlled, erosion makes maintenance harder and negatively affects software evolution.ObjectiveTo study the effects of architecture erosion on a large software project and determine whether search-based module clustering might reduce the conceptual distance between the current architecture and the design-time one.MethodTo run an exploratory study with Apache Ant. First, we characterize Ant’s evolution in terms of size, change dispersion, cohesion, and coupling metrics, highlighting the potential introduction of architecture and code-level problems that might affect the cost of changing the system. Then, we reorganize the distribution of Ant’s classes using a heuristic search approach, intending to re-emerge its design-time architecture.ResultsIn characterizing the system, we observed that its original, simple design was lost due to maintenance and the addition of new features. In optimizing its architecture, we found that current models used to drive search-based software module clustering produce complex designs, which maximize the characteristics driving optimization while producing class distributions that would hardly be acceptable to developers maintaining Ant.ConclusionThe structural perspective promoted by the coupling and cohesion metrics precludes observing the adequate software module clustering from the perspective of software engineers when considering a large open source system. Our analysis adds evidence to the criticism of the dogma of driving design towards high cohesion and low coupling, at the same time observing the need for better models to drive design decisions. Apart from that, we see SBSE as a learning tool, allowing researchers to test Software Engineering models in extreme situations that would not be easily found in software projects.     

Derivation of local software quality metrics (software quality circles)

Software is a product in serious need of quality control technology. Major effort notwithstanding, software engineering has produced few metrics for aspects of software quality that have the potential of being universally applicable. The present paper suggests that, although universal metrics are elusive, metrics that are applicable and useful in a fully defined setting are readily available. A theory is presented that a well-defined software work group can articulate their operational concept of quality and derive useful metrics for that concept and their environment.     

软件复用度量综述

软件复用度量是软件复用技术中不可分割的一部分,在软件复用开发中占据重要地位。软件复用开发与传统的软件开发方式不同,从而影响到软件度量,因此需要新的软件复用度量方法,软件复用度量的研究已经引起学术界的广泛重视。本文是一篇软件复用度量综述,阐述了软件复用对度量的影响以及有关软件复用的度量。     

A case study of tracking software development using quality metrics

This paper presents a case history of Mentor Graphics using a set of quality metrics to track development progress for a recent major software release. It provides background on how Mentor Graphics originally began using software metrics to measure product quality, how this became accepted, and how these metrics later fell out of favour. To restore these metrics to effective use, process changes were required for setting quality and metric targets, and for the way the metrics are used for tracking development progress. With these process changes in place, and the addition of a new metric, the case history demonstrates that the metric set could be used effectively to indicate problems in this release and help manage changes to the plan for completion of the release. The lessons learned in this case history are presented, along with subsequent data that further validates these metrics.     

Empirical evaluation of reuse sensitiveness of complexity metrics

Measuring software products and processes is essential for improving software productivity and quality. In order to evaluate the complexity of object-oriented software, several complexity metrics have been proposed. Among them, Chidamber and Kemerer’s metrics are the most well-known for object-oriented software. Their metrics evaluate the complexity of the classes in terms of internal, inheritance, and coupling complexity. Though the reused classes of the class library usually have better quality than the newly-developed ones, their metrics deal with inheritance and coupling complexity in the same way. This article first proposes a revision of the Chidamber and Kemerer’s metrics which can be applied to software which had been constructed by reusing software components. Then, we give an analysis of data collected from the development of an object-oriented software using a GUI framework. We compare the original metrics with the revised ones by evaluating the accuracy of estimating the effort to fix faults and show the validity and usefulness of the revised metrics.