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.

 

【电力系统】单机无穷大电力系统短路故障暂态稳定Simulink仿真(带说明文档)内容概要:本文档围绕“单机无穷大电力系统短路故障暂态稳定Simulink仿真”展开,提供了完整的仿真模型与说明文档,重点研究电力系统在发生短路故障后的暂态稳定性问题。通过Simulink搭建单机无穷大系统模型,模拟不同类型的短路故障(如三相短路),分析系统在故障期间及切除后的动态响应,包括发电机转子角度、转速、电压和功率等关键参数的变化,进而评估系统的暂态稳定能力。该仿真有助于理解电力系统稳定性机理,掌握暂态过程分析方法。; 适合人群:电气工程及相关专业的本科生、研究生,以及从事电力系统分析、运行与控制工作的科研人员和工程师。; 使用场景及目标:①学习电力系统暂态稳定的基本概念与分析方法;②掌握利用Simulink进行电力系统建模与仿真的技能;③研究短路故障对系统稳定性的影响及提高稳定性的措施(如故障清除时间优化);④辅助课程设计、毕业设计或科研项目中的系统仿真验证。; 阅读建议:建议结合电力系统稳定性理论知识进行学习,先理解仿真模型各模块的功能与参数设置,再运行仿真并仔细分析输出结果,尝试改变故障类型或系统参数以观察其对稳定性的影响,从而深化对暂态稳定问题的理解。
本研究聚焦于运用MATLAB平台,将支持向量机(SVM)应用于数据预测任务,并引入粒子群优化(PSO)算法对模型的关键参数进行自动调优。该研究属于机器学习领域的典型实践,其核心在于利用SVM构建分类模型,同时借助PSO的全局搜索能力,高效确定SVM的最优超参数配置,从而显著增强模型的整体预测效能。 支持向量机作为一种经典的监督学习方法,其基本原理是通过在高维特征空间中构造一个具有最大间隔的决策边界,以实现对样本数据的分类或回归分析。该算法擅长处理小规模样本集、非线性关系以及高维度特征识别问题,其有效性源于通过核函数将原始数据映射至更高维的空间,使得原本复杂的分类问题变得线性可分。 粒子群优化算法是一种模拟鸟群社会行为的群体智能优化技术。在该算法框架下,每个潜在解被视作一个“粒子”,粒子群在解空间中协同搜索,通过不断迭代更新自身速度与位置,并参考个体历史最优解和群体全局最优解的信息,逐步逼近问题的最优解。在本应用中,PSO被专门用于搜寻SVM中影响模型性能的两个关键参数——正则化参数C与核函数参数γ的最优组合。 项目所提供的实现代码涵盖了从数据加载、预处理(如标准化处理)、基础SVM模型构建到PSO优化流程的完整步骤。优化过程会针对不同的核函数(例如线性核、多项式核及径向基函数核等)进行参数寻优,并系统评估优化前后模型性能的差异。性能对比通常基于准确率、精确率、召回率及F1分数等多项分类指标展开,从而定量验证PSO算法在提升SVM模型分类能力方面的实际效果。 本研究通过一个具体的MATLAB实现案例,旨在演示如何将全局优化算法与机器学习模型相结合,以解决模型参数选择这一关键问题。通过此实践,研究者不仅能够深入理解SVM的工作原理,还能掌握利用智能优化技术提升模型泛化性能的有效方法,这对于机器学习在实际问题中的应用具有重要的参考价值。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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