[Book Review] "A Guide to Good Code" - A Book for Learning the Judgment Criteria for Good Code Necessary in AI-Driven Development

Hello, I'm Tsukuboshi (tsukuboshi0755)!

I recently read "A Roadmap to Good Code," and I felt it was a book that lets you systematically learn the essence of good code, so I'd like to introduce it!

In recent years, code generation using generative AI has spread rapidly, and opportunities for humans to write code from scratch are definitely decreasing.

While opportunities to review AI-generated code have increased, don't you ever find yourself uncertain, wondering "Can this AI-generated code really be called good code?"

In this article, I'll introduce the recommended highlights of this book, which teaches you the "criteria for judging good code" that is useful in the age of AI-driven development!

Book Information

• Release date: 2025/5/29
• Authors: Atsushi Mori, Fumina Kuno
• Publisher: MynaBi Publishing

https://book.mynavi.jp/ec/products/detail/id=147381

Note that the sample code in this book is written in Kotlin, but in principle it only uses simple syntax that would be used in other languages as well, and explanations are provided for Kotlin-specific features.

As I mentioned, my main languages are Python and TypeScript and I have no development experience in Kotlin, but I was able to read through it without any issues.

Therefore, even if you don't normally write Kotlin, as long as you regularly work with some programming language, this is content you can read through without particular difficulty.

Target Readers

I would like to recommend this book to those who are using code output by coding agents like Claude Code, but are having trouble determining whether that code is good or not.

The main reason I particularly recommend this book is that the concepts and general principles for judging good code are compiled in a necessary, sufficient, and compact manner.

While the way code is written can reflect personal preferences when you dig into the details, this book limits its explanations to universally applicable content, and it is a book that condenses the knowledge you want to have at minimum when discussing good code.

I especially recommend reading this book if you feel anxious and wonder, "I've confirmed that the AI-generated code works. But can we put this into production and maintain it as-is?"

Recommended Highlights of This Book

You Can Understand the Significance of Writing Good Code

The fundamental question of why "good code" is needed in the first place is organized in Chapter 1 of this book.

Here, the impact that good code ultimately has on product longevity and team productivity is discussed from the axes of maintainability, ease of change, and technical debt.

In the present day when AI generates large amounts of code, being able to explain "why we write good code in the first place" in your own words is a foundational axis for making adoption decisions about AI output.

You Can Learn the Principles and Techniques That Constitute Good Code

From naming that conveys intent, to separating responsibilities and organizing dependencies, and further to rules for preventing careless mistakes — the specific principles for evaluating code as "good" are consolidated here.

This theme is covered in Chapters 2 through 7 of this book, and the main discussion points are as follows.

• Variable naming conventions, comment notation, domain objects
• Separation of concerns, cyclomatic complexity, cohesion
• Directories and modules
• Dependencies, DIP, coupling
• KISS, YAGNI, DRY
• Magic numbers, types, mutable and immutable, SSOT, CQS

These are perspectives that are directly useful when evaluating AI-generated code based on "whether it is structured in a form that is easy to change."

You Can Acquire the Design and Mechanisms That Support Code That Keeps Running

Furthermore, this book explains not just creating code that works, but also concepts and mechanisms that affect all development team members in order to nurture code that keeps working.

The relevant content is consolidated in Chapters 8 through 11, and mainly covers the following topics.

• Boy Scout Rule
• Layered architecture, clean architecture
• Automated testing, AAA, test coverage, mocks, fakes, and stubs
• Git, code reviews, coding conventions, linters and formatters, Design Docs

These serve as criteria for maintaining long-term quality across the entire project, going beyond the pros and cons of individual pieces of code.

In Closing

This time I introduced "A Roadmap to Good Code."

This book systematically covers naming, decomposition, dependencies, architecture, testing, and team development, and you will build the foundational strength to confidently evaluate code written by AI.

Since the general knowledge necessary for judging good code is compiled in an easy-to-understand manner, the content will make you want to read it over and over again.

This is a book I can particularly recommend for engineers who want to be able to judge "can this really be called good code?" with their own criteria when it comes to AI-generated code.

I hope you'll pick it up and give it a read!

That's all from Tsukuboshi (tsukuboshi0755)!