一种基于SPC的软件过程质量分析方法

一个项目的开发活动是很多软件过程的集合,不同软件过程之间关联性很强,成功地分析特定软件过程质量的关键是确保软件过程分析的独立性,剔除来自于其他过程的影响。传统Shewhart控制图基于统计假设检验理论,能够区分软件过程中的偶然因素和系统因素,但Shewhart控制图是全控图,无法区分过程之间的影响。为解决这种问题,定义软件过程的总质量和分质量,把系统因素细分为外部系统因素和内部系统因素,并总结软件过程质量诊断表,以使用控制图和选控图来帮助诊断导致软件过程质量异常的偏差源。     

自相关过程SPC控制及其ARL的Monte-Carlo分析

对受控自相关过程建立其时间序列模型,并得出自相关过程的预测及预测误差。其次,通过Shewhart控制图原理验证预测误差的独立性;最后讨论了对均值发生阶跃型故障的自相关过程的SPC控制,并通过Monte-Carlo模拟,对Shewhart控制图以及EWMA控制图的ARL进行了深入地比较分析。     

基于软件过程能力度指数的软件过程控制图

在软件过程度量领域,统计过程控制图是进行度量分析的主要工具。统计过程控制图的核心问题是控制界限问题,控制界限的大小决定着统计过程控制图的灵敏度和有效性,过大和过小都不能达到有效过程控制的效果。论文在分析传统统计过程控制图存在的缺陷的基础上,以软件过程能力决定软件过程表现的思想为出发点,提出了一种新的软件过程控制图——软件过程能力度指数控制图并进行了实例验证。实例分析证明此控制图是非常有效的。     

基于静态小波的控制图异常模式分析

针对工业过程信号时频特性多尺度的特点,以及现有各种控制图的单尺度性,本文提出了一种基于静态小波分析和Shewhart控制图相结合的过程监控方法。首先运用静态小波分析把信号分解到需要的深度,提取信号特征,再对各尺度上的特征向量运用Shewhart控制图进行监控,最后采用同样的方法监控重建信号,进而判断过程当前状态是否受控。本文通过分析控制图的突变和趋势两种异常模式的平均链长ARL,说明该多尺度方法的有效性。     

Q控制图分析和控制软件过程的方法研究

控制图被广泛用以判定软件过程度量数据的异常点,而软件过程的分析和控制方法目前也只使用了控制图这一种,但使用控制图进行过程分析前我们必须首先采集大量的数据以得出正态分布的参数,这一点使其不适合在数据采集量小、生命周期短、但对度量偏差要求反应及时的软件过程中使用。为了解决这一问题,本文提出使用Q控制图分析和控制软件过程的方法,并指出相较控制图而言,Q控制图更适合用于管理软件过程,并给出具体算法和实例。     

基于统计过程控制的软件测试技术研究

软件测试是软件开发过程中的重要组成部分，测试工作对软件质量有直接影响。利用统计过程控制的基本原理。以实例分析使用Shewhart控制图对测试过程进行控制的方法。     

基于SPC的软件过程度量技术的研究

本文将统计过程控制原理SPC应用于软件过程度量。在介绍SPC原理的基础上,讨论了休哈特控制图的构成和分析方法,结合实例,深入分析了SPC在软件过程度量中的应用。     

软件演化过程建模中的开发人员度量方法

提出了一种在构建形式化的软件演化过程模型(formal software evolution process model,FSEPM)中使用X-S图来度量开发人员的统计控制方法.开发人员是构建模型的核心角色,度量其相关属性可以透析和严格管理开发过程.通过一个实例说明如何度量处在构建过程中的开发人员,利用度量结果来分析开发过程的稳定性、找出可归属原因、进行预测和估计、并为整个开发过程的演化奠定基础.     

二阶自相关过程Shewhart 型控制图的性能评价

针对二阶自相关过程,分别采用有限马氏链内嵌法和积分法给出了Shewhart型修正控制图和残差控制图平均运行链长的计算方法,并通过其数值结果的比较分析,得到结论:当自相关过程系数均为正值时,修正图的性能较好;当过程系数均取负值时,残差图较为适用;当过程系数符号相异时,两图性能可采用所给方法具体比较.该结论为控制图的选择和应用提供了理论依据.     

应用统计过程控制监控软件测试过程

统计过程控制(SPC)是通过使用控制图来制定过程决策和预测过程行为的一种质量控制方法.SPC的方法用于软件过程,可以通过描述过程行为来监控过程的稳定性.讨论了将SPC应用于软件测试过程,针对测试过程中所度量的不同分布形式的数据而采用不同计算方式应用SPC的控制图,然后根据控制图判断测试过程是否稳定,并分析可能存在的可归属原因.     

Using cause selecting control charts to monitor dependent process stages with attributes data

In this study, we propose cause selecting control charts to monitor two dependent process stages with attributes data. The control limits on the bivariate binomial control region can be obtained. The detection ability of the cause selecting control charts is compared to those of Shewhart attributes control charts and the bivariate binomial control region by different correlation. Numerical example and simulation study show that the cause selecting control charts perform better than Shewhart attributes control charts and the bivariate binomial control region.     

A practical view of software measurement and implementationexperiences within Motorola

A practical view of software measurement that formed the basis for a companywide software metrics initiative within Motorola is described. A multidimensional view of measurement is provided by identifying different dimensions (e.g., metric usefulness/utility, metric types or categories, metric audiences, etc.) that were considered in this companywide metrics implementation process. The definitions of the common set of Motorola software metrics, as well as the charts used for presenting these metrics, are included. The metrics were derived using the goal/question metric approach to measurement. A distinction is made between the use of metrics for process improvement over time across projects and the use of metrics for in-process project control. Important experiences in implementing the software metrics initiative within Motorola are also included     

Enhancing the performance of CUSUM scale chart

Control charts act as the most important statistical process monitoring tool, widely used for the purpose of identifying unusual variations in process parameters. Researchers have implemented different rules to increase the sensitivity of Shewhart, CUSUM and EWMA control charts for the detection of small shifts in process location. However, for the monitoring of process scale, the use of such rules has been limited to Shewhart charts. This study proposes the implementation of sensitizing rules in CUSUM scale charts to enhance their ability to detect smaller changes in process variability. The performance of the proposed schemes is evaluated and compared with the simple scale CUSUM scheme, the EWMS chart, the M-EWMS chart and the COMB chart, in terms of run length characteristics such as average run length (ARL) and standard deviation of the run length distribution (SDRL). Control chart coefficients to set the ARL at the desired level are also provided. Two numerical examples are given to illustrate the application of the proposed schemes on practical data sets.     

应用软件质量工程中过程度量的研究

过程度量是软件开发过程中实施软件质量保证(SQA)的一个重要课题。文章对过程度量的概念进行了一些介绍,讨论了在开发应用软件过程中常用的几种度量,可供软件开发部门在实践SQA时参考。     

On efficient use of auxiliary information for control charting in SPC

In Statistical Process Control (SPC), monitoring of the process dispersion has a major impact on the performance of processes like manufacturing, management and services. Control charts act as the most important SPC tool, used to differentiate between common and special cause variations in the process. The use of auxiliary information can enhance the detection ability of control charts and hence an efficient monitoring of process parameter(s) can be done. This study deals with the Shewhart type variability control charts based on auxiliary characteristics for the non-cascading processes, assuming stability of auxiliary parameters. The control chart structures of these variability charts are provided and their performance evaluations are carried out in terms of average run length (ARL), relative average run length (RARL) and extra quadratic loss (EQL) under the normal and t distributed process environments. The comparisons have been made among different variability charts and superiorities are established based on their detection abilities for different amounts of shifts in process dispersion. An illustrative example is also provided in support of the theory, and finally the study ends with concluding remarks and suggestions for future research.     

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。     

Variable control scheme in the cascade processes

The article considers the variables process control scheme for cascade processes. We construct variable sample sizes and sampling intervals (VSSI) control charts to effectively monitor the input variable and the output variable produced by a cascade process. The performance of the proposed VSSI control charts is measured by the adjusted average time to signal derived by a Markov chain approach. An example of the metallic film thickness of the computer connectors system shows the application and the performance of the proposed VSSI control charts in detecting shifts in means of the cascade process. Furthermore, the performance of the proposed VSSI control charts and the fixed sample sizes and sampling intervals control charts are compared by numerical analysis results. These demonstrate that the former is much faster in detecting small and medium shifts. The optimum VSSI control charts are also proposed using optimization technique when quality engineers cannot specify the values of the variable sample sizes and sampling intervals. It has been found that the optimum VSSI control charts work and are thus suggested whenever quality engineers cannot specify the values of variable sample sizes and sampling intervals. Furthermore, the impacts of misusing Shewhart charts to monitoring the process means on the cascade process are also investigated.     

Q control charts for negative binomial distribution

As we known, Q charts are SPC charts for approximate normal distribution. This paper proposes new kinds of Q charts for negative binomial random variable. First, define two transformations of approximate standard normal distribution for a negative binomial parameter p which it is known or unknown. Then, Q charts are constructed by transformed data, these Q charts are useful for monitoring process in real time from the beginning of sampling, especially, for p unknown in advance. In order to compare the goodness of the Q charts with conventional Shewhart charts with 3-sigma limits, an example is given. The results have shown that these Q charts are more efficient than classical Shewhart charts. Finally, some review are given.