Good Code, Bad Code: Think Like a Software Engineer, MEAP Version 3

Good Code, Bad Code is a shortcut guide to writing high-quality code. Your mentor is Google veteran Tom Long, who lays out lessons and mindsets that will take your code from “junior developer” to “senior engineer.” This instantly-useful book distils the principles of professional coding into one comprehensive and hands-on beginner’s guide.You’ll start with a jargon-free primer to coding fundamentals that teaches you to think about abstractions, consider your fellow engineers, and write code that can recover from errors. Next, you’ll dive into specific techniques and practices. You’ll run through common coding practices to learn when to apply the right technique to your problem—and which might be best avoided! All practices are illustrated with annotated code samples written in an instantly recognizable pseudocode that you can relate to your favorite object-oriented language. By the time you’re done, you’ll be writing the kind of readable, reusable, and testable code that’s the mark of a true software professional. Good Code, Bad Code MEAP V03 Copyright Welcome Brief Contents Chapter 1: Code quality 1.1 How code becomes software 1.2 The goals of code quality 1.2.1 Code should work 1.2.2 Code should keep working 1.2.3 Code should be adaptable to changing requirements 1.2.4 Code should not reinvent the wheel 1.3 The pillars of code quality 1.3.1 Make code readable 1.3.2 Avoid surprises 1.3.3 Make code hard to misuse 1.3.4 Make code modular 1.3.5 Make code reusable and generalizable 1.3.6 Make code testable and test it properly 1.4 Does writing high-quality code slow us down? 1.5 Summary Chapter 2: Layers of abstraction 2.1 Nulls and the pseudocode convention in this book 2.2 Why use layers of abstraction? 2.2.1 Layers of abstraction and the pillars of code quality 2.3 Layers of code 2.3.1 APIs and implementation details 2.3.2 Functions 2.3.3 Classes 2.3.4 Interfaces 2.3.5 When layers get too thin 2.4 What about microservices? 2.5 Summary Chapter 3: Thinking about other engineers 3.1 Your code and other engineers’ code 3.1.1 Things that are obvious to you are not obvious to others 3.1.2 Other engineers will inadvertently try to break your code 3.1.3 In time, you will forget about your own code 3.2 How will others figure out how to use your code? 3.2.1 Looking at the names of things 3.2.2 Looking at the data types of things 3.2.3 Reading documentation 3.2.4 Asking you in person 3.2.5 Looking at your code 3.3 Code contracts 3.3.1 Small print in contracts 3.3.2 Don’t rely too much on small print 3.4 Checks and assertions 3.4.1 Checks 3.4.2 Assertions 3.5 Summary Chapter 4: Errors 4.1 Recoverability 4.1.1 Errors that can be recovered from 4.1.2 Errors that cannot be recovered from 4.1.3 Often only the caller knows if an error can be recovered from 4.1.4 Make callers aware of errors they might want to recover from 4.2 Robustness vs failure 4.2.1 Fail fast 4.2.2 Fail loudly 4.2.3 Scope of recoverability 4.2.4 Don’t hide errors 4.3 Ways of signalling errors 4.3.1 Recap: Exceptions 4.3.2 Explicit: checked exceptions 4.3.3 Implicit: unchecked exceptions 4.3.4 Explicit: nullable return type 4.3.5 Explicit: result return type 4.3.6 Explicit: outcome return type 4.3.7 Implicit: promise or future 4.3.8 Implicit: returning a magic value 4.4 Signalling errors that can’t be recovered from 4.5 Signalling errors that a caller might want to recovered from 4.5.1 Arguments for using unchecked exceptions 4.5.2 Arguments for using explicit techniques 4.5.3 My personal opinion: use an explicit technique 4.6 Don’t ignore compiler warnings 4.7 Summary Chapter 5: Make code readable 5.1 Use descriptive names 5.1.1 Non-descriptive names make code hard to read 5.1.2 Comments are a poor substitute for descriptive names 5.1.3 Solution: make names descriptive 5.2 Use comments appropriately 5.2.1 Redundant comments can be harmful 5.2.2 Comments are a poor substitute for readable code 5.2.3 Comments can be great for explaining why code exists 5.2.4 Comments can provide useful high-level summaries 5.3 Don’t fixate on number of lines of code 5.3.1 Avoid succinct but unreadable code 5.3.2 Solution: make code readable, even if it requires more lines 5.4 Stick to a consistent coding style 5.4.1 An inconsistent coding style can cause confusion 5.4.2 Solution: adopt and follow a style guide 5.5 Avoid deeply nesting code 5.5.1 Deeply nested code can be hard to read 5.5.2 Solution: restructure to minimize nesting 5.5.3 Nesting is often a result of doing too much 5.5.4 Solution: break code into smaller functions 5.6 Make function calls readable 5.6.1 Arguments can be hard to decipher 5.6.2 Solution: use named arguments 5.6.3 Solution: use descriptive types 5.6.4 Sometimes there’s no great solution 5.6.5 What about the IDE? 5.7 Avoid using unexplained values 5.7.1 Unexplained values can be confusing 5.7.2 Solution: use a well-named constant 5.7.3 Solution: use a well-named function 5.8 Use anonymous functions appropriately 5.8.1 Anonymous functions can be great for small things 5.8.2 Anonymous functions can be hard to read 5.8.3 Solution: use named-functions instead 5.8.4 Large anonymous functions can be problematic 5.8.5 Solution: break large anonymous functions into named-functions 5.9 Use shiny, new language features appropriately 5.9.1 New features can improve code 5.9.2 Obscure features can be confusing 5.9.3 Use the best tool for the job 5.10 Summary Chapter 6: Avoid surprises 6.1 Avoid returning magic values 6.1.1 Magic values can lead to bugs 6.1.2 Solution: return null or an optional 6.1.3 Sometimes magic values can happen accidentally 6.2 Use the null-object pattern appropriately 6.2.1 Returning an empty collection can improve code 6.2.2 Returning an empty string can sometimes be problematic 6.2.3 More complicated null-objects can cause surprises 6.2.4 A null-object implementation can cause surprises 6.3 Avoid causing unexpected side-effects 6.3.1 Side-effects that are obvious and intentional are fine 6.3.2 Unexpected side-effects can be problematic 6.3.3 Solution: avoid a side-effect or make it obvious 6.4 Beware of mutating input parameters 6.4.1 Mutating an input parameter can lead to bugs 6.4.2 Solution: copy things before mutating them 6.5 Avoid writing misleading functions 6.5.1 Doing nothing when a critical input is missing can cause surprises 6.5.2 Solution: make critical inputs required 6.6 Future-proof enum handling 6.6.1 Implicitly handling future enum values can be problematic 6.6.2 Solution: use an exhaustive switch-statement 6.6.3 Beware of the default case 6.6.4 Caveat: Relying on another project’s enum 6.7 Can’t we just solve all this with testing? 6.8 Summary Chapter 7: Make code hard to misuse 7.1 Consider making things immutable 7.1.1 Mutable classes can be easy to misuse 7.1.2 Solution: set values only at construction time 7.1.3 Solution: Use a design pattern for immutability 7.2 Consider making things deeply immutable 7.2.1 Deep mutability can lead to misuse 7.2.2 Solution: defensively copy things 7.2.3 Solution: use immutable data structures 7.3 Avoid overly general data types 7.3.1 Overly general types can be misused 7.3.2 Pair types are easy to misuse 7.3.3 Solution: use a dedicated type 7.4 Dealing with time 7.4.1 Representing time with integers can be problematic 7.4.2 Solution: use appropriate data structures for time 7.5 Have single sources of truth for data 7.5.1 Second sources of truth can lead to invalid states 7.5.2 Solution: use primary data as the single source of truth 7.6 Have single sources of truth for logic 7.6.1 Multiple sources of truth for logic can lead to bugs 7.6.2 Solution: have a single source of truth 7.7 Summary Chapter 8: Make code modular 8.1 Consider using dependency injection 8.1.1 Hard-coded dependencies can be problematic 8.1.2 Solution: use dependency injection 8.1.3 Design code with dependency injection in mind 8.2 Prefer depending on interfaces 8.2.1 Depending on concrete implementations limits adaptability 8.2.2 Solution: depend on interfaces where possible 8.3 Beware of class inheritance 8.3.1 Class inheritance can be problematic 8.3.2 Solution: use composition 8.3.3 What about genuine is-a relationships? 8.4 Classes should care about themselves 8.4.1 Caring too much about other classes can be problematic 8.4.2 Solution: make classes care about themselves 8.5 Encapsulate related data together 8.5.1 Unencapsulated data can be difficult to handle 8.5.2 Solution: group related data into objects or classes 8.6 Beware of leaking implementation details in return types 8.6.1 Leaking implementation details in a return type can be problematic 8.6.2 Solution: return a type appropriate to the layer of abstraction 8.7 Beware of leaking implementation details in exceptions 8.7.1 Leaking implementation details in exceptions can be problematic 8.7.2 Solution: make exceptions appropriate to the layer of abstraction 8.8 Summary Chapter 9: Make code reusable and generalizable 9.1 Beware of assumptions 9.1.1 Assumptions can lead to bugs when code is reused 9.1.2 Solution: avoid unnecessary assumptions 9.1.3 Solution: if an assumption is necessary, enforce it 9.2 Beware of global state 9.2.1 Global state can make reuse unsafe 9.2.2 Solution: dependency inject shared state 9.3 Use default return values appropriately 9.3.1 Default return values in low-level code can harm reusability 9.3.2 Solution: provide defaults in higher-level code 9.4 Keep function parameters focused 9.4.1 A function that takes more than it needs can be hard to reuse 9.4.2 Solution: make functions take only what they need 9.5 Consider using generics 9.5.1 Depending on a specific type limits generalizability 9.5.2 Solution: use generics 9.6 Summary Chapter 10: Unit testing principles 10.1 Unit testing primer 10.2 What makes a good unit test? 10.2.1 Accurately detects breakages 10.2.2 Agnostic to implementation details 10.2.3 Well-explained failures 10.2.4 Understandable test code 10.2.5 Easy and quick to run 10.3 Focus on the public API, but don’t ignore important behaviors 10.3.1 Important behaviors might be outside the public API 10.4 Test doubles 10.4.1 Reasons for using a test double 10.4.2 Mocks 10.4.3 Stubs 10.4.4 Mocks and stubs can be problematic 10.4.5 Fakes 10.4.6 Schools of thought on mocking 10.5 Pick and choose from testing philosophies 10.6 Summary Chapter 11: Unit testing practices 11.1 Test behaviors not just functions 11.1.1 One test case per function is often inadequate 11.1.2 Solution: concentrate on testing each behavior 11.2 Avoid making things visible just for testing 11.2.1 Testing private functions is often a bad idea 11.2.2 Solution: prefer testing via the public API 11.2.3 Solution: split the code into smaller units 11.3 Test one behavior at a time 11.3.1 Testing multiple behaviors at once can lead to poor tests 11.3.2 Solution: test each behavior in its own test case 11.3.3 Parameterized tests 11.4 Use shared test setup appropriately 11.4.1 Shared state can be problematic 11.4.2 Solution: avoid sharing state or reset it 11.4.3 Shared configuration can be problematic 11.4.4 Solution: define important configuration within test cases 11.4.5 When shared configuration is appropriate 11.5 Use appropriate assertion matchers 11.5.1 Inappropriate matchers can lead to poorly explained failures 11.5.2 Solution: use an appropriate matcher 11.6 Use dependency injection to aid testability 11.6.1 Hard-coded dependencies can make code impossible to test 11.6.2 Solution: use dependency injection 11.7 Some final words on testing 11.8 Summary Appendix A: Chocolate brownie recipe Appendix B: Null safety and optionals B.1 Using null-safety B.1.1 Checking for nulls B.2 Using optional Appendix C: Extra code examples C.1 The builder pattern Practical techniques for writing code that is robust, reliable, and easy for team members to understand and adapt.Summary In Good Code, Bad Code you'll learn how to: Think about code like an effective software engineer Write functions that read like well-structured sentences Ensure code is reliable and bug free Effectively unit test code Identify code that can cause problems and improve it Write code that is reusable and adaptable to new requirements Improve your medium and long-term productivity Save yourself and your team time The difference between good code or bad code often comes down to how you apply the established practices of the software development community. In Good Code, Bad Code you'll learn how to boost your productivity and effectiveness with code development insights normally only learned through careful mentorship and hundreds of code reviews. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Software development is a team sport. For an application to succeed, your code needs to be robust and easy for others to understand, maintain, and adapt. Whether you're working on an enterprise team, contributing to an open source project, or bootstrapping a startup, it pays to know the difference between good code and bad code. About the book Good Code, Bad Code is a clear, practical introduction to writing code that's a snap to read, apply, and remember. With dozens of instantly-useful techniques, you'll find coding insights that normally take years of experience to master. In this fast-paced guide, Google software engineer Tom Long teaches you a host of rules to apply, along with advice on when to break them! What's inside Write functions that read like sentences Ensure your code stays bug-free How to sniff out bad code Save time for yourself and your team About the reader For coders early in their careers who are familiar with an object-oriented language, such as Java or C#. About the author Tom Long is a software engineer at Google where he works as a tech lead. Among other tasks, he regularly mentors new software engineers in professional coding best practices. Table of Contents PART 1 IN THEORY 1 Code quality 2 Layers of abstraction 3 Other engineers and code contracts 4 Errors PART 2 IN PRACTICE 5 Make code readable 6 Avoid surprises 7 Make code hard to misuse 8 Make code modular 9 Make code reusable and generalizable PART 3 UNIT TESTING 10 Unit testing principles 11 Unit testing practices Practical techniques for writing code that is robust, reliable, and easy for team members to understand and adapt. Good code or bad code? The difference often comes down to how you apply the conventions, style guides, and other established practices of the software development community. In Good Code, Bad Code youll learn how to boost your effectiveness and productivity with code development insights normally only learned through years of experience, careful mentorship, and hundreds of code reviews. In Good Code, Bad Code youll learn how to: + Think about code like an effective software engineer + Write functions that read like a well-structured sentence + Ensure code is reliable and bug free + Effectively unit test code + Identify code that can cause problems and improve it + Write code that is reusable and adaptable to new requirements + Improve your medium and long-term productivity + Save you and your teams time Good Code, Bad Code Lays Out Lessons And Mindsets That Will Take Your Code From “junior Developer” To “senior Engineer.” Good Code Or Bad Code? The Difference Often Comes Down To How You Apply The Conventions, Style Guides, And Other Established Practices Of The Software Development Community. In Good Code, Bad Code You’ll Learn How To Boost Your Effectiveness And Productivity With Code Development Insights Normally Only Learned Through Years Of Experience, Careful Mentorship, And Hundreds Of Code Reviews. Good Code, Bad Code Lays Out Lessons And Mindsets That Will Take Your Code From “junior Developer” To “senior Engineer.” This Instantly-useful Book Distils The Principles Of Professional Coding Into One Comprehensive And Hands-on Beginner’s Guide. Purchase Of The Print Book Includes A Free Ebook In Pdf, Kindle, And Epub Formats From Manning Publications. "Software development is a team sport. For an application to succeed, your code needs to be robust and easy for others to understand, maintain, and adapt. Whether you're working on an enterprise team, contributing to an open source project, or bootstrapping a startup, it pays to know the difference between good code and bad code. Good Code, Bad Code is a clear, practical introduction to writing code that's a snap to read, apply, and remember"--Page 4 of cover