计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 48-52.

• 综述研究 • 上一篇    下一篇

模式驱动的软件架构设计研究综述

张英杰, 朱雪峰   

  1. 中国石油大学北京石油数据挖掘北京市重点实验室 北京102249;
    中国石油大学北京地球物理与信息工程学院 北京102249
  • 出版日期:2019-02-26 发布日期:2019-02-26
  • 作者简介:张英杰(1992-),男,硕士生,主要研究方向为软件开发;朱雪峰(1973-),男,博士,硕士生导师,主要研究方向为软件可靠性,E-mail:xuefeng.zhu@cup.edu.cn。

Review of Pattern Driven Software Architecture Design

ZHANG Ying-jie, ZHU Xue-feng   

  1. Beijing Key Lab of Petroleum Data Mining,China University of Petroleum,Beijing 102249,China;
    College of Geophysics and Information Engineering,China University of Petroleum,Beijing 102249,China
  • Online:2019-02-26 Published:2019-02-26

摘要: 在目前的软件开发理论和实践过程中,软件生产从需求获取到代码完成都需要人工完成。从需求分析到体系结构的对应与转换依然依赖于软件设计者的技能、经验和创造力;大多数软件代码的生产仍然需要依靠程序员来人工完成。这种传统的软件生产方式为软件产业带来了许多问题。随着软件工程理论和case工具的发展,突破传统软件开发方式的方法论逐步被提出。基于模式的软件自动化生产方式能够在从软件抽象模型到软件代码自动生成的过程中节省大量人力,提高软件开发效率,增加软件的自适应性。通过介绍基于模式的软件自动化生产方式来重点研究软件架构的设计。

关键词: 开发效率, 设计模式, 体系结构, 自动化生产, 自适应

Abstract: In the current software development theory and practice,software production needs to be done manually from aquistion of requirement to code completion.The mapping from software requirements analysis to software architectures still needs designer’s skills,experience and creativity.Most software code production still depends on the programmer to do it manually.This traditional way of software production poses many problems for the software industry.With the development of software engineering theory and case tools,the methodology of breaking through traditional way of software development has been put forward gradually.Software automation production methods based on pattern can save a lot of manpower in the process of the software abstract model to the automatic generation of software code.This approach improves the efficiency of software development and increases the adaptability of the software.This paper stu-died the design of model-driven software architecture by introducing mode-based software automation production me-thods.

Key words: Adaptation, Architecture, Automated production, Design pattern, Development efficiency

中图分类号: 

  • TP31
[1]Model-driven Architecture[EB/OL].[2017-08-25].https://en.wikipedia.org/wiki/Model-driven_architecture.
[2]Software factory[EB/OL].[2017-08-26].https://en.wikipedia.org/wiki/Software_factory.
[3]郭新峰,马世龙,吕江花,等.需求变更自动化管理模型与实现[J].计算机系统应用,2015,24(4):11-18.
[4]刘奎,宋淼,陈一飞,等.基于软件模式的PIM到PSM的模型变换[J].计算机技术与发展,2006,16(10):74-76.
[5]BUSCHMANN F,MEUNIER R,ROHNERT H,et al.Pattern-Oriented Software Architecture(Volume 1):A System of Patterns [M].New York:John Wiley & Sons,1996.
[6]MICHAEL J.Problem Frames:Analyzing and Structuring Software Development Problem [M].Addison-Wesley,2001.
[7]模式[EB/OL].[2017-07-12].http://www.mie168.com/zhua-nti/moshi.htm.
[8]模式[EB/OL].[2014-06-24].http://www.baike.com/wiki/模式.
[9]STEPHEN W.Software Requirement Patterns [M].Microsoft Press,2014.
[10]FOWLER M.分析模式[M].北京:机械工业出版社,2004.
[11]ALEXANDER C.The Timeless Way of Building [M].Oxford University Press,1979.
[12]ERICH G,RICHARD H,RALPH J,et al.Design Patterns-Elements of Reusable Object-Oriented Software [M].Addison-Wesley,1995.
[13]BUSCHMANN F,HENNEY K,SCHMIDT D,et al.Pattern-Oriented Software Architecture(Volume 5):On Patterns and Patterns Languages [M].New York:John Wiley & Sons,2007.
[14]丁博,王怀民,史殿习.构造具备自适应能力的软件[J].软件学报,2013,24(9):1981-2000.
[15]KRAMER J,MAGEE J.Self-Managed systems:An architectural challenge[C]∥Proceedings of the Conference on the Future of Software Engineering.2007.
[16]RAMIREZ A J.Design patterns for developing dynamically adaptive systems [M].Michigan State University,2008.
[17]SCHMIDT D,STAL M,ROHNERT H,et al.Pattern-Oriented Software Architecture(Volume 2):Patterns for Concurrent and Networked Objects[M].New York:John Wiley & Sons,2001.
[18]GOMAA H,HUSSEIN M.Software reconfiguration patterns for dynamic evolution of software architectures[J].Fourth Working IEEE/IFIP Conference on Software Architecture,2004(WICSA 2004).2004.
[19]WEGNER P.Research Directions In Software Technology[C]∥Proceedings of The 3rd International Conference on Software Engineering.1978.
[1] 史殿习, 赵琛然, 张耀文, 杨绍武, 张拥军.
基于多智能体强化学习的端到端合作的自适应奖励方法
Adaptive Reward Method for End-to-End Cooperation Based on Multi-agent Reinforcement Learning
计算机科学, 2022, 49(8): 247-256. https://doi.org/10.11896/jsjkx.210700100
[2] 刘高聪, 罗永平, 金培权.
基于热点数据的持久性内存索引查询加速
Accelerating Persistent Memory-based Indices Based on Hotspot Data
计算机科学, 2022, 49(8): 26-32. https://doi.org/10.11896/jsjkx.210700176
[3] 陈俊, 何庆, 李守玉.
基于自适应反馈调节因子的阿基米德优化算法
Archimedes Optimization Algorithm Based on Adaptive Feedback Adjustment Factor
计算机科学, 2022, 49(8): 237-246. https://doi.org/10.11896/jsjkx.210700150
[4] 王杰, 李晓楠, 李冠宇.
基于自适应注意力机制的知识图谱补全算法
Adaptive Attention-based Knowledge Graph Completion
计算机科学, 2022, 49(7): 204-211. https://doi.org/10.11896/jsjkx.210400129
[5] 唐枫, 冯翔, 虞慧群.
基于自适应知识迁移与资源分配的多任务协同优化算法
Multi-task Cooperative Optimization Algorithm Based on Adaptive Knowledge Transfer andResource Allocation
计算机科学, 2022, 49(7): 254-262. https://doi.org/10.11896/jsjkx.210600184
[6] 谭任深, 徐龙博, 周冰, 荆朝霞, 黄向生.
海上风电场通用运维路径规划模型优化及仿真
Optimization and Simulation of General Operation and Maintenance Path Planning Model for Offshore Wind Farms
计算机科学, 2022, 49(6A): 795-801. https://doi.org/10.11896/jsjkx.210400300
[7] 周天清, 岳亚莉.
超密集物联网络中多任务多步计算卸载算法研究
Multi-Task and Multi-Step Computation Offloading in Ultra-dense IoT Networks
计算机科学, 2022, 49(6): 12-18. https://doi.org/10.11896/jsjkx.211200147
[8] 傅思清, 黎铁军, 张建民.
面向粒子输运程序加速的体系结构设计
Architecture Design for Particle Transport Code Acceleration
计算机科学, 2022, 49(6): 81-88. https://doi.org/10.11896/jsjkx.210600179
[9] 高越, 傅湘玲, 欧阳天雄, 陈松龄, 闫晨巍.
基于时空自适应图卷积神经网络的脑电信号情绪识别
EEG Emotion Recognition Based on Spatiotemporal Self-Adaptive Graph ConvolutionalNeural Network
计算机科学, 2022, 49(4): 30-36. https://doi.org/10.11896/jsjkx.210900200
[10] 赵亮, 张洁, 陈志奎.
基于双图正则化的自适应多模态鲁棒特征学习
Adaptive Multimodal Robust Feature Learning Based on Dual Graph-regularization
计算机科学, 2022, 49(4): 124-133. https://doi.org/10.11896/jsjkx.210300078
[11] 林利祥, 刘旭东, 刘少腾, 徐跃东.
前向纠错编码在网络传输协议中的应用综述
Survey on the Application of Forward Error Correction Coding in Network Transmission Protocols
计算机科学, 2022, 49(2): 292-303. https://doi.org/10.11896/jsjkx.210500104
[12] 陈乐, 高岭, 任杰, 党鑫, 王祎昊, 曹瑞, 郑杰, 王海.
基于自适应码率移动增强现实应用的能效优化研究
Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality
计算机科学, 2022, 49(1): 194-203. https://doi.org/10.11896/jsjkx.201100107
[13] 刘凯, 张宏军, 陈飞琼.
基于领域适应嵌入的军事命名实体识别
Name Entity Recognition for Military Based on Domain Adaptive Embedding
计算机科学, 2022, 49(1): 292-297. https://doi.org/10.11896/jsjkx.201100007
[14] 梁剑, 何军辉.
基于宏块编码信息自适应置换的H.264/AVC视频加密方法
H.264/AVC Video Encryption Based on Adaptive Permutation of Macroblock Coding Information
计算机科学, 2022, 49(1): 314-320. https://doi.org/10.11896/jsjkx.201100089
[15] 赵敏, 刘惊雷.
基于高斯场和自适应图正则的半监督聚类
Semi-supervised Clustering Based on Gaussian Fields and Adaptive Graph Regularization
计算机科学, 2021, 48(7): 137-144. https://doi.org/10.11896/jsjkx.200800190
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!