CMM学习笔记 - 18 KPAs

本文介绍了软件能力成熟度模型中的四个关键级别:可重复级、已定义级、已管理级和优化级。每个级别详细列举了实现该级别的关键过程域,如需求管理、项目计划跟踪等,有助于组织改进其软件开发流程。

Repeatable Level

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RM: To establish understanding of requirements among all parties; stabilize software requirements and clarify the impact of changes on the project’s cost and schedule.

 

Software Project Planning (SPP): To establish complete and reasonable project plans; improve cost and schedule estimates and document the project activities.

 

Software Project Tracking & Oversight (SPTO): To provide visibility into actual progress and oversight to enable timely corrective action.

 

Software Quality Assurance (SQA): Help the development team state clearly what it plans to do and the standards and conventions it wants to adopt and follow, and ensure the team follows its plans, standards and conventions; and identify and correct discrepancies when they occur.

 

Software Configuration Management (SCM): To establish and maintain the integrity of the
software products throughout the life cycle.

 

Software Subcontract Management (SSM): To support selection of qualified subcontractors and effective management of their activities.

 

 

Defined Level

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Organization Process Focus (OPF): To establish the organizational responsibility for software process activities those improve the organization’s overall software process.

 

Organization Process Definition (OPD): To develop and maintain a usable set of software process assets that improves process performance across the projects and provide a basis for cumulative, long-term benefits to the organization.

 

Training Program (TP): To develop the skills and knowledge of individuals so they can perform their roles effectively and efficiently.

 

Integrated Software Management (ISM): To integrate the software engineering and management activities into a coherent, defined software process tailored from OPD.

 

Software Product Engineering (SPE): To consistently perform a well-defined engineering process that integrates all the software engineering activities to produce correct, consistent software products effectively and efficiently.

(Here is where the software engineering work gets done (processes written and followed) for design, code, test.)

 

Intergroup Coordination (IC): To establish a means for the software engineering group to participate actively with the other groups so the project is better able to satisfy the customer’s needs effectively and efficiently.

 

Peer Review (PR): To remove defects from the software work products early and efficiently.  Also develop a better understanding of the software work products and of defects that might be prevented.

 

 

Managed Level

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Software Quality Management (SQM): To develop a quantitative understanding of the quality of the project’s software products and achieve specific quality goals

 

Quantitative Process Management (QPM): To control the process performance of the software project quantitatively.

 

 

Optimizing Level

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Defect Prevention (DP): To identify the cause of defects and prevent them from recurring.

 

Technology Change Management (TCM): To identify new technologies (I.e. tools, methods, and processes) and transition them into the organization.

Process Change Management (PCM): To continually improve the software processes used in the organization with the intent of improving software quality, increasing productivity, and decreasing the cycle time for product development.

 

根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
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