Clean Code in JavaScript
eBook - ePub

Clean Code in JavaScript

Develop reliable, maintainable, and robust JavaScript

James Padolsey

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  1. 548 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Clean Code in JavaScript

Develop reliable, maintainable, and robust JavaScript

James Padolsey

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About This Book

Get the most out of JavaScript for building web applications through a series of patterns, techniques, and case studies for clean coding

Key Features

  • Write maintainable JS code using internal abstraction, well-written tests, and well-documented code
  • Understand the agents of clean coding like SOLID principles, OOP, and functional programming
  • Explore solutions to tackle common JavaScript challenges in building UIs, managing APIs, and writing states

Book Description

Building robust apps starts with creating clean code. In this book, you'll explore techniques for doing this by learning everything from the basics of JavaScript through to the practices of clean code. You'll write functional, intuitive, and maintainable code while also understanding how your code affects the end user and the wider community.

The book starts with popular clean-coding principles such as SOLID, and the Law of Demeter (LoD), along with highlighting the enemies of writing clean code such as cargo culting and over-management. You'll then delve into JavaScript, understanding the more complex aspects of the language. Next, you'll create meaningful abstractions using design patterns, such as the Class Pattern and the Revealing Module Pattern. You'll explore real-world challenges such as DOM reconciliation, state management, dependency management, and security, both within browser and server environments. Later, you'll cover tooling and testing methodologies and the importance of documenting code. Finally, the book will focus on advocacy and good communication for improving code cleanliness within teams or workplaces, along with covering a case study for clean coding.

By the end of this book, you'll be well-versed with JavaScript and have learned how to create clean abstractions, test them, and communicate about them via documentation.

What you will learn

  • Understand the true purpose of code and the problems it solves for your end-users and colleagues
  • Discover the tenets and enemies of clean code considering the effects of cultural and syntactic conventions
  • Use modern JavaScript syntax and design patterns to craft intuitive abstractions
  • Maintain code quality within your team via wise adoption of tooling and advocating best practices
  • Learn the modern ecosystem of JavaScript and its challenges like DOM reconciliation and state management
  • Express the behavior of your code both within tests and via various forms of documentation

Who this book is for

This book is for anyone who writes JavaScript, professionally or otherwise. As this book does not relate specifically to any particular framework or environment, no prior experience of any JavaScript web framework is required. Some knowledge of programming is assumed to understand the concepts covered in the book more effectively.

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Section 1: What is Clean Code Anyway?

In this section, we'll discuss the purpose of code and the tenets of it, such as clarity and maintainability. We'll also cover the very broad challenge of naming things, as well as some of the valuable questions and hazards to watch out for.
This section contains the following chapters:
  • Chapter 1, Setting the Scene
  • Chapter 2, The Tenets of Clean Code
  • Chapter 3, The Enemies of Clean Code
  • Chapter 4, SOLID and Other Principles
  • Chapter 5, Naming Things Is Hard

Setting the Scene

JavaScript was created by Brendan Eich in 1995, with the goal of being a glue language. It was intended to help web designers and amateurs easily manipulate and derive behavior from their HTML. JavaScript was able to do this via the DOM API, a set of interfaces provided by the browser that would give access to the parsed representation of HTML. Soon after this, DHTML became the popular term, referring to the more dynamic user interfaces that JavaScript enabled: everything from animated rollover button states to client-side form validation. Eventually came the rise of Ajax, which enabled communication between the client and the server. This opened up a considerable fountain of potential applications. The web, previously purely the domain of documents, was now on the way to becoming a powerhouse of processor- and memory-intensive applications:
In 1995, nobody could have predicted that JavaScript would one day be used to build complex web applications, program robots, query databases, write plugins for photo manipulation software, and be behind one of the most popular server runtimes in existence, Node.js.
In 1997, not long after its creation, JavaScript was standardized by Ecma International under the name ECMAScript, and it is still undergoing frequent changes under the TC39 committee. Most recent versions of the language have been named according to the year of their release, such as ECMAScript 2020 (ES2020).
Due to its burgeoning capabilities, JavaScript has attracted a passionate community that drives its growth and ubiquity. And due to its considerable popularity, there are now countless different ways to do the same thing in JavaScript. There are thousands of popular frameworks, libraries, and utilities. The language too is changing on a near-constant basis in reaction to the increasing demands of its applications. This creates a great challenge: among all of this change, while being pushed and pulled in different directions, how can we know how to write the best possible code? Which frameworks should we use? What conventions should we employ? How should we test our code? How should we craft sensible abstractions?
To answer these questions, we need to briefly go back to basics. And that is the purpose of this chapter. We'll be discussing the following:
  • What the true purpose of the code is
  • Who our users are and what problems they have
  • What it means to write code for humans

Why we write code

At its simplest, we know that programming is about instructing computers, but what are we instructing them to do? And to what end? And what other purposes does code serve?
We can broadly say that code is a way of solving problems. By writing code, we are expressing a complex task or series of actions, distilling them into a singular process that can be easily utilized by a user. So we can say that the code is an expression of a problem domain. We can even say it is a form of communication, a way to relay information and intent. Understanding that code is a complex thing with many complementary purposes, such as problem-solving and communication, will enable us to use it to its fullest potential. Let's delve further into this complexity by exploring what we mean when we speak of code as a method of relaying intent.

Code as intent

We often think of code as simply a series of instructions that are executed by a computer. But in many ways, this misses the true magic of what we're doing when we write code. When we convey instructions, we are expressing our intent to the world; we are saying These are the things that I want to occur.
Humans have been conveying instructions for as long as they've been around. One example of this is a simple cooking recipe:
Cut about three hundred grams of butter (small cubes!)
Take 185 grams dark chocolate
Melt it with butter over a saucepan
Break half dozen eggs, ideally large ones
Mix them together with a few cups of sugar
Instructions like these are quite easy to understand for a human, but you'll notice they follow no strict specification. The measuring units are inconsistent, as is the punctuation and the wording. And some of the instructions are quite ambiguous and therefore open to misinterpretation by someone who hasn't cooked before:
  • What constitutes a large egg?
  • When should I consider the butter fully melted?
  • How dark should the dark chocolate be?
  • How small is a small cube of butter?
  • What does over a saucepan mean?
Humans can usually muddle through such ambiguities with their initiative and experience, but machines aren't so adept. A machine must be instructed with enough specificity to carry out every step. What we wish to communicate to a machine is our intent, that is, please do this thing, but due to the nature of machines, we must be utterly specific. Thankfully, how we choose to write these instructions is up to us; there are many programming languages and approaches, and almost all of them were created with the goal of making it easier for humans to communicate their intent in a less burdensome way.
The distance between human capability and computing capability is quickly narrowing. The advent of machine learning, natural language processing, and highly specialized programs means that machines are far more flexible in the types of instructions they can carry out. However, code will continue to be useful for some time, as it allows us to communicate in a highly specific and standardized way. With this high level of specificity and consistency, we can have more faith that our instructions will be executed as intended, every time.

Who is the user?

No meaningful conversation about programming can occur without considering the user. The user, whether they are a fellow programmer or the end user of a UI, is at the core of what we do.
Let's imagine that we are tasked with validating user-inputted shipping addresses on a website. This particular website sells medication to hospitals around the world. We're in a bit of a rush and would prefer to use something that someone else has implemented. We find a publicly available package called shipping_address_validator and decide to use it.
If we had taken the time to check the code within the package, in its postal code validation file, we would have seen this:
function validatePostalCode(code) {
return /^[0-9]{5}(?:-[0-9]{4})?$/.test(code);
This validatePostalCode function happens to be using regular expressions (also known as RegExp and regex), delimited by forward slashes, to define a pattern of characters to match a string against. You can read more about these constructs in Chapter 6, Primitive and Built-In Types.
Unfortunately, due to our haste, we didn't question the functionality of the shipping_address_validator package. We assumed it did what it says on the tin. One week after releasing our code to production we get a bug report saying that some users are unable to enter their address information. We look at the code and realize, to our horror, that it only validates US ZIP codes, not all countries' postal codes (for example, it doesn't work on UK postcodes, such as GR82 5JY).
Through this unfortunate series of events, this piece of code is now responsible for blocking the shipment of vital medication to customers all over the world, numbering in the thousands. Fortunately, fixing it doesn't take too long.
Forgetting for a moment who is responsible for this mishap, I'd like to pose the following question: who are the users of this code?
  • We, the programmers, who decided to use the shipping_address_validator package?
  • The unwitting customers who are attempting to enter their addresses?
  • The patients in the hospitals who are left waiting for their medication?
There isn't a clear-cut answer to this question. When bugs appear in the code, we can see how there can be massive unfortunate downstream effects. Should the original programmer of the package be concerned with all these downstream dependencies? When a plumber is hired to fix a tap on a sink, should they only consider the function of the tap itself, or the sink into which it pours?
When we write code, we are defining an implicit specification. This specification is communicated by its name, its configuration options, its inputs, and its outputs. Anyone who uses our code has the right to expect it to ...

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