Refactoring: Improving the Design of Existing Code (2nd Edition) (Addison-Wesley Signature Series (Fowler))
Authors: Martin Fowler
ISBN-10: 0134757599
ISBN-13: 9780134757599
Edition 版次: 2
Publication Date 出版日期: 2018-12-07
Print Length 页数: 448 pages
For more than twenty years,experienced programmers worldwide have relied on Martin Fowler’s Refactoring to improve the design of existing code and to enhance software maintainability,as well as to make existing code easier to understand.
This eagerly awaited new edition has been fully updated to reflect crucial changes in the programming landscape. Refactoring,Second Edition,features an updated catalog of refactorings and includes JavaScript code examples,as well as new functional examples that demonstrate refactoring without classes.
Like the original,this edition explains what refactoring is; why you should refactor; how to recognize code that needs refactoring; and how to actually do it successfully,no matter what language you use.
Understand the process and general principles of refactoring
Quickly apply useful refactorings to make a program easier to comprehend and change
Recognize “bad smells” in code that signal opportunities to refactor
Explore the refactorings,each with explanations,motivation,mechanics,and simple examples
Build solid tests for your refactorings
Recognize tradeoffs and obstacles to refactoring
Refactoring Improving the Design of Existing Code,2nd Edition 9780134757599. zip[/erphpdown]
Refactoring Improving the Design of Existing Code,2nd Edition
相关推荐
- Mastering Unity Game Development with C#: Harness the full potential of Unity 2022 game development using C#
- Autodesk Civil 3D 2025 Unleashed: Elevate your civil engineering designs and advance your career with Autodesk Civil 3D
- Unlock Your Creativity with Photopea: Edit and retouch images, and create striking text and designs with the free online software
- Federated Learning for Future Intelligent Wireless Networks
- Facilitating Software Architecture: Empowering Teams to Make Architectural Decisions
- Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach