基于SVN的应用软件持续集成

随着软件开发技术的发展,软件配置管理和持续集成已经成为软件开发过程中的一个重要组成部分；为了在软件开发过程中正确应用这些新技术,需要开展这方面的研究工作；结合工作实践,以SVN作为配置管理工具,分析了持续集成工具ICP-CI的特点、部署方式和运行机制；详细叙述了ICP-CI持续集成构建工程的搭建过程,搭建过程包括配置管理工具SVN客户端安装、基于SVN的代码更新、静态检查、编译、打包,版本包的自动化测试；构建工程的各个阶段都可能出现错误,导致构建失败,通过对构建失败原因的分析,将构建失败分为3类并给出相应的解决方案；最后介绍了一个典型工作案例；工作实践表明在软件的开发过程中采用基于SVN的持续集成,可以提高软件质量和软件开发效率,降低软件开发成本。     

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。     

基于指标融合的集成软件开发质量度量方法研究

为了提高集成软件开发质量度量方法的度量效果，提出基于指标融合的集成软件开发质量度量方法。建立软件质量度量指标，采用指标融合的方法对指标进行处理，利用中介加权法确定真区域和假区域，求出评价矩阵的可靠度评价参数集，获取软件开发质量度量的综合评分集，并按照得分大小排序，得到可靠性的量化评分结果，以此完成集成软件开发质量度量。实验结果表明，所提方法的度量准确度较高，并在5 min之内就能够完成软件开发质量度量，并且减少了软件开发指标度量后软件发生故障的次数，从而提高了集成软件开发质量方法的度量效果。     

软件可修改性在软件开发过程中的控制*

在传统的软件可修改性定义的基础上提出了基于软件开发过程的软件可修改性,确定了垂直软件可修改性和水平软件可修改性的关系和基本度量方法。建立了基于软件开发过程的软件可修改性模型,得到了软件开发过程各阶段中软件可修改性的度量方法,从而为在软件开发过程中控制软件的可修改性提供了基础。     

一种基于CMMI软件过程管理框架的度量系统

通过分析CMMI模型4类过程域的相互关系,将软件开发过程融入到CMMI工程过程域中,设计出软件过程管理自动化框架.在此基础上将度量引入到各过程域中,构建了软件度量系统,研究了度量的选择和定义、度量数据采集自动化的实现以及度量数据的分析,通过实例说明度量在软件过程改进中的作用.     

基于度量的软件过程改进研究

在软件开发的整个过程中,需要对各个阶段进行评估、分析、改进,它们是一个不断循环的过程.即软件过程改进贯穿于软件开发的始终.如何对软件过程进行度量、度量元是什么,已经成为当前国内外研究的热点.对当前国内许多机构开展的质量管理体系认证模型进行研究,提出了一种基于CMMI的6σ软件度量框架,在此基础上分析了软件过程改进和评估中的关键问题.通过对软件过程的改进,从而提高软件产品质量和提升软件开发效率.     

一个基于GDM方法的集成软件过程度量模型

为满足软件企业不同角色的度量需求,基于GDM方法提出了一个面向企业应用的集成软件过程度量模型,模型包含组织结构定义、角色职责定义、GDM分析结果、业务目标和度量目标评价函数等基础元素,定义了元素间的关联关系.模型支持项目度量元收集范围的确立,维护了组织内数据加工及数据分析流程,可以为角色定制度量分析视图,并辅助确定过程改善活动的目标过程.基于此模型,开发了企业集成软件过程度量系统,在系统支持下,企业度量活动的自动化程度及效率得到了提高.     

基于生产线方式的软件开发

计算机在不同行业的广泛使用,需要大量的软件作为支撑。越来越多的软件企业发现按照传统的方式即一次开发一个软件的方式已经不能够满足需求,它们需要提高软件开发的效率,而基于生产线方式的软件开发可以较好地解决这个问题。本文介绍了基于生产线方式开发软件的基本概念及其优点,软件生产线与软件成熟度模型集成的关系,以及实施软件生产线过程中可能用到的度量标准,最后指出实施软件生产线过程中可能引入的风险。     

一种软件持续集成平台的技术方案

本文介绍了在敏捷软件开发过程中使用的一种软件持续集成平台的技术方案。该方案利用开源软件Hudson和SVN构建平台,在改进软件质量和降低风险,缩短缺陷引入、发现和修复之间的时间间隔方面取得了显著效果。     

软件开发过程持续改进的方法研究

随着软件的规模和复杂度的不断增大，软件开发过程越来越难以度量和保持持续改进。通过对软件开发过程中软件度量计划、度量实施、度量分析的研究，提出了一种软件开发过程持续改进的螺旋式模型，用于促使软件开发过程的持续改进和软件组织能力的有效提升。     

Efficiency of CAMEtools in software quality assurance

Our paper describes the requirements and possibilities of integration of metrics tools in the field of software quality assurance. Tools for the support of the measurement process are herein classified as Computer Assisted Software Measurement and Evaulation Tools (CAMETools). Software measurement regarded as a special type of metrics application provides a great amount of basic information for the evaluation of the software development process or the software product itself. Our paper examines the effectiveness and destination of software measurement in tool-based software development and is based on an analysis of more than 20 CAME tools in the Software Measurement Laboratory at the University of Magdeburg. CAMEtools are useable for the process, product, and resources evaluation in all phases of the software life cycle (including the problem definition) for different development paradigms. The efficiency of CAME tools is described on the basis of a general measurement framework. This framework includes all steps in the software measurement and evaulation process: metrics definition, selection of the evaluation criteria, tool-based modelling and measurement, value presentation and statistical analysis. The framework includes the main aspects of the process evaluation techniques (Capability Maturity Model, ISO 9000-3 etc.) and product evaluation (ISO 9126, etc.). It is not a disjointed set of aspects: our measurement framework represents an incremental technique for the application of quantification of quality aspects in a required quality assurance     

基于CruiseControl的持续集成实现方案

软件的开发过程决定着软件质量。在任何软件开发过程中都有一个重要的组成部分：得到可靠的软件创建版本。在分析传统集成模式的弊端基础上,提出采用持续集成的观点。阐明了持续集成的基础理论以及与持续集成相关的常见的误区和概念。分析了持续集成工具CruiseControl工作原理,最后提出了一种基于CruiseControl的持续集成实现方案。     

模糊聚类算法在软件度量数据分析中的应用

为了提高软件质量,控制和改汕软件开发过程,需要有效地度量软件开发过程和分析其过程各个阶段收集的度量数据。文中将模糊聚类算法应用到软件度量的数据分析中。先给出了数据挖掘相关知识和理论,再介绍了该算法在软件度量数据分析中应用的实验研究。由于较快地发现有严重缺陷的模块,进而提高了软件测试效率。     

基于软件体系结构的可复用构件制作和组装

软件复用和基于构件的软件开发是软件工程化开发和工业化生产的必然趋势,可复用构件的制作和集成组装是其中涉及的两个重要活动.首先分析了现有的构件集成方式所存在的不足,引出了基于体系结构的构件集成;接着对几种不同的体系结构类型进行比较,阐述了它们对构件集成的影响;然后提出了支持接口连接式和插头插座式体系结构类型的构件模型以及基于体系结构的构件集成组装框架,并给出了构件的制作和组装过程;最后对工作进行总结,并指出了进一步的研究方向.     

基于演化数据的软件缺陷预测性能改进

软件持续演化已经是不争的事实,演化意味着需求的变化,也就必然导致了缺陷的不断产生.现有的缺陷预测技术多偏重于基于软件工作制品,如文档、代码、测试用例等的属性来预测缺陷,但如果把软件看作一种物种,其生命周期内的演化本质上是一个物种的逐步进化,其缺陷的表现也必然带着该物种的特征,而且还受到进化历史中的演化轨迹的影响.已有一些研究人员开始研究软件演化过程,并提出了一些演化度量元.研究和提出了可以刻画软件演化轨迹的两类演化度量元,并通过案例研究,建立缺陷预测模型.在6个著名开源软件数据集上训练和验证了由软件演化度量元建立的缺陷预测模型,获得了良好的预测性能,验证了演化度量元对缺陷预测性能的改进.     

基于抽象语法树的软件度量工具的设计与实现

为了提高软件质量和控制，改善软件开发过程，文中提出了以抽象语法树为核心的软件度量工具(ASTMT)的体系结构图，介绍了度量工具的功能及特点，详细论述了工具中各个模块的设计与实现方法，着重讨论了抽象语法树的组成及其节点设计。介绍该工具在软件开发中的实际应用，进而验证了工具的实用性与有效性。     

An evaluation model for dependability of Internet-scale software on basis of Bayesian Networks and trustworthiness

Internet-scale software becomes more and more important as a mode to construct software systems when Internet is developing rapidly. Internet-scale software comprises a set of widely distributed software entities which are running in open, dynamic and uncontrollable Internet environment. There are several aspects impacting dependability of Internet-scale software, such as technical, organizational, decisional and human aspects. It is very important to evaluate dependability of Internet-scale software by integrating all the aspects and analyzing system architecture from the most foundational elements. However, it is lack of such an evaluation model. An evaluation model of dependability for Internet-scale software on the basis of Bayesian Networks is proposed in this paper. The structure of Internet-scale software is analyzed. An evaluating system of dependability for Internet-scale software is established. It includes static metrics, dynamic metrics, prior metrics and correction metrics. A process of trust attenuation based on assessment is proposed to integrate subjective trust factors and objective dependability factors which impact on system quality. In this paper, a Bayesian Network is build according to the structure analysis. A bottom-up method that use Bayesian reasoning to analyses and calculate entity dependability and integration dependability layer by layer is described. A unified dependability of the whole system is worked out and is corrected by objective data. The analysis of experiment in a real system proves that the model in this paper is capable of evaluating the dependability of Internet-scale software clearly and objectively. Moreover, it offers effective help to the design, development, deployment and assessment of Internet-scale software.     

软件质量的度量问题

度量的目的是为软件产品提供量化的测量结果，以避免主观论断的差错。软件度量是软件维护的基础。本文对软件度量的定义、度量的过程、面向对象软件的度量、度量与维护的关系等进行了讨论。     

A learning algorithm for optimizing continuous integration development and testing practice

Continuous integration, at its core, includes a set of practices that aim to prevent and reduce the cost of software integration issues by merging working software copies often. Regression testing is considered a good practice in software development with continuous integration, which ensures that code changes are not negatively affecting software functionality. As, nowadays, software development is carried out iteratively, with small code increments continuously developed and regression tested, it is of critical importance that continuous regression testing is time efficient. However, in practice, regression testing is often long lasting and faces scalability problems as software grows larger or as software changes are made more frequently. One contributing factor to these issues is test redundancy, which causes the same software functionality being tested multiple times across a test suite. In large-scale software, especially highly configurable software, redundancy in continuous regression testing can significantly grow the size of test suites and negatively affect the cost effectiveness of continuous integration. This paper presents a practical learning algorithm for optimizing continuous integration testing by reducing ineffective test redundancy in regression suites. The novelty of the algorithm lies in learning and predicting the fault-detection effectiveness of continuous integration tests using historical test records and combining this information with coverage-based redundancy metrics. The goal is to identify ineffective redundancy, which is maximally reduced in the resulting regression test suite, thus reducing test time and improving the performance of continuous integration. We apply and evaluate the algorithm in two industrial projects of continuous integration. The results show that the proposed algorithm can improve the efficiency of continuous integration practice in terms of decreasing test execution time by 38% on average compared to the industry practice of our case study and by 40% on average compared to the retest-all approach. The results further demonstrate no significant reduction in fault-detection effectiveness of continuous regression testing. This suggests that the proposed algorithm contributes to the state of the practice in the continuous integration development and testing of highly configurable systems.