The Rules of Programming: How to Write Better Code

Description

This philosophy-of-programming guide presents a unique and entertaining take on how to think about programming. A collection of 21 pragmatic rules, each presented in a stand-alone chapter, captures the essential wisdom that every freshly minted programmer needs to know and provides thought-provoking insights for more seasoned programmers. Author Chris Zimmerman, cofounder of the video game studio Sucker Punch Productions, teaches basic truths of programming by wrapping them in memorable aphorisms and driving them home with examples drawn from real code. This practical guide also helps managers looking for ways to train new team members. The rules in this book include:   As simple as possible, but no simplerLet your code tell its own storyLocalize complexityGeneralization takes three examplesWork backward from your result, not forward from your codeThe first lesson of optimization is don't optimizeA good name is the best documentationBugs are contagiousEliminate failure casesCode that isn't running doesn't workSometimes you just need to hammer the nails Cover Copyright Table of Contents Preface Girls Who Code Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments The Story of the Rules How to Disagree with the Rules Rule 1: As Simple as Possible, but No Simpler Measuring Simplicity …But No Simpler Sometimes It’s Better to Simplify the Problem Rather than the Solution Simple Algorithms Don’t Lose the Plot One Rule to Rule Them All Rule 2: Bugs Are Contagious Don’t Count on Your Users Automated Testing Can Be Tricky Stateless Code Is Easier to Test Audit State You Can’t Eliminate Don’t Trust the Caller Keeping Your Code Healthy Rule 3: A Good Name Is the Best Documentation Don’t Optimize for Minimal Keystrokes Don’t Mix and Match Conventions Don’t Shoot Yourself in the Foot Don’t Make Me Think Rule 4: Generalization Takes Three Examples YAGNI An Obvious Objection to This Strategy, in Response to Which I Double Down It’s Actually Worse than YAGNI This Is Not What Success Looks Like Rule 5: The First Lesson of Optimization Is Don’t Optimize The First Lesson of Optimization The Second Lesson of Optimization Putting the Second Lesson to the Test Step 1: Measure and Attribute Processor Time Step 2: Make Sure There’s Not a Bug Step 3: Measure Your Data Step 4: Plan and Prototype Step 5: Optimize and Repeat Applying the Five-Step Optimization Process There Is No Third Lesson of Optimization Interlude: In Which the Previous Chapter Is Criticized Rule 6: Code Reviews Are Good for Three Reasons Code Reviews Are About Sharing Knowledge The Forbidden Code Review The True Value of the Code Review Code Reviews Are Inherently Social Rule 7: Eliminate Failure Cases A Function That Makes It Easy to Shoot Myself in the Foot Shooting Myself in the Foot via a Ricochet Enlisting the Compiler’s Aid to Avoid Shooting My Foot Timing Is Everything A More Complicated Example Making Ordering Mistakes Impossible Using Templates Instead of Method Chaining Coordinated Control of State Detecting Mistakes Is Good, but Making Them Impossible to Express Is Better Rule 8: Code That Isn’t Running Doesn’t Work Step 1: A Simple Beginning Step 2: Generalizing a Common Pattern Step 3: Adding Disguises Step 4: The Chickens Return Home to Roost Assigning Blame The Limits of Testing Rule 9: Write Collapsible Code This Is What Failure Feels Like The Role of Short-Term Memory Where to Draw the Line The Cost of Abstraction Use Abstraction to Make Things Easier to Understand The Role of Long-Term Memory Common Knowledge Is Free; New Concepts Are Expensive Putting It All Together Rule 10: Localize Complexity A Simple Example Hiding Internal Details Distributed State and Complexity Capacitated? Things Start to Get Foggy Rethinking the Approach Localized Complexity, Simple Interactions Rule 11: Is It Twice as Good? Three Paths Forward: Ignore, Tweak, or Refactor Gradual Evolution Versus Continual Reinvention A Simple Rule of Thumb Dealing with Fuzzy Benefits Rework Is a Good Opportunity to Fix Small Problems Rule 12: Big Teams Need Strong Conventions Formatting Conventions Language Usage Conventions Problem-Solving Conventions Effective Teams Think Alike Rule 13: Find the Pebble That Started the Avalanche The Lifecycle of a Bug Minimizing State Dealing with Unavoidable State Dealing with Unavoidable Delay Rule 14: Code Comes in Four Flavors Easy Problem, Simple Solution Easy Problem, Three Complicated Solutions The Cost of Complexity The Four (But Really Three) Kinds of Programmers Hard Problem, Somewhat Complicated Solutions That Don’t Work Hard Problem, Somewhat Complicated Solution Hard Problem, Simple Solution Rule 15: Pull the Weeds Weed Identification How Code Gets Weedy Rule 16: Work Backward from Your Result, Not Forward from Your Code An Example An Annoyance Appears Choosing a Side of the Gap Working Backward Instead And Now for Something Completely Different Working Forward and Working Backward Rule 17: Sometimes the Bigger Problem Is Easier to Solve Jumping to Conclusions Finding a Clear Path Forward Recognizing the Opportunity Rule 18: Let Your Code Tell Its Own Story Don’t Tell Stories That Aren’t True Make Sure There’s a Point to the Story Telling Good Stories Rule 19: Rework in Parallel Bumps in the Road Build a Parallel System Instead A Concrete Example Stack Allocation in Practice A Cloud on the Horizon Making Stack Contexts a Little Smarter Migrating from Old Stack Contexts to New Ones Preparing to Migrate StackVector Time to Migrate Recognizing When Parallel Rework Is a Good Strategy Rule 20: Do the Math To Automate or Not to Automate Look for Hard Limits When the Math Changes When the Math Problem Changes Back into a Word Problem Rule 21: Sometimes You Just Need to Hammer the Nails A New Argument There’s Never Just One Bug The Siren Call of Automation Managing File Sizes There Are No Shortcuts Conclusion: Making the Rules Your Own Use Your Best Judgment Discuss Amongst Yourselves Signing Off Appendix A. Reading C++ for Python Programmers Types Formatting and Comments Comments Indentation and Split Lines Boolean Operations Lists Increment Operators Classes Visibility Declarations and Definitions Function Overloading Templates Pointers and References Appendix B. Reading C++ for JavaScript Programmers Types Arrays Classes Declarations and Definitions Function Overloading Templates Pointers and References Index About the Author