Javascript

Introduction to Testing in Node.js with Mocha and Chai

Published Apr 21, 2021

Updated May 30, 2023

5 min read

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

When writing software, it can be tedious to test features manually and ensure your entire program continues to work as it evolves over time. We can use automated testing to invoke our program automatically, and ensure it works as expected.

The Purpose of Testing

Simply put, writing good tests that verify your program works as expected will help you catch regressions, and improve the reliability of your software. Good tests will ensure the program meets the requirements the program needs to meet, and that it will continue to do so as the program evolves over time. Software testing is also integral to some software development processes like TDD and BDD, though these are outside the scope of this article.

The Node.js ecosystem has a large variety of testing frameworks to choose from, and many serve many different purposes. We'll be focusing on using Mocha and Chai to demonstrate the basics of testing in the context of backend development.

What are Mocha and Chai?

Mocha is a JavaScript testing framework that runs on Node.js, and in the browser. It is one of the most popular test frameworks in use in the Node.js community, and is very easy to learn and understand.

Chai is an assertion library that helps extend test frameworks like Mocha to make writing certain kinds of tests easier. Most test frameworks like Mocha also come with their own assertion functions as well, but we've opted to show off Chai since it makes it easier to write more complex tests.

Installing Mocha and Friends

Mocha and Chai can be installed into your project using the following:

npm install mocha chai --save-dev

Also, note that all following examples are available on GitHub for your viewing pleasure. I won't be showing all code being tested in this article as it would be a lot. You can then define a scripts section in your package.json file to run your tests so you can run your tests with npm test.

{
  "name": "mocha-basic",
  "version": "1.0.0",
  "description": "",
  "main": "index.js",
  "scripts": {
    "test": "mocha"
  },
  "author": "",
  "license": "ISC",
  "devDependencies": {
    "mocha": "^8.3.2"
  }
}

The Basics

We will start by showing off one of the most basic tests that you can make with Mocha, and bear with me as I'll show off some more complex tests later. This is a basic unit test that calls a function that returns the number 42, and tests that the result comes back as expected. The following is defined in a file called index.js.

exports.meaningOfLife = () => {
  return 42;
};

And the test for this function can be written as follows in a file located at test/index.test.js (note the naming convention).

const assert = require("assert");

const { meaningOfLife } = require("../index");

describe("Index", function () {
  describe("#meaningOfLife", function () {
    it("should return 42", function () {
      const result = meaningOfLife();
      assert.strictEqual(result, 42);
    });
  });
});

Tests can be run by invoking npm test in your project root. I've opted to keep all tests in a directory called test under the project root, and have test files for each corresponding source file with an added test.js suffix at the end of the filename. There are a few layers to this test, so let's go through each one.

Tests are defined by calling global Mocha functions that are defined when the mocha command is invoked. describe is used for organizing your tests. The way that I'm using it above is one level for what I'm testing in general, this being the index file, the meaningOfLife function, and then all tests that need to invoke that function. In this case there's only one test since this is a very simple function.

Tests are written inside of callbacks passed into the it function. One thing to note is how the test reads out like plain English. This is meant to make the purpose of the test to be more clear to the reader. In this example, we simply import and call the meaningOfLife function and check that it equals 42. We also use the assert call from Mocha to do this comparison, as it will raise an exception if the two expressions don't match.

Let's test an API

To demonstrate more practical tests, I've opted to create a basic API that returns data, and tests that the data has the expected format. The API is a simple express application that returns an array of movies as JSON by calling GET /movies against it.

Testing an API isn't as simple as calling a function, and checking a response, so we will use chai and supertest to help us. The tests are as follows:

const chai = require("chai");
const request = require("supertest");

const expect = chai.expect;
const app = require("../../app");

describe("Movies", function () {
  describe("GET /movies", function () {
    it("should return 200 OK with several movies", async function () {
      const response = await request(app)
        .get("/movies")
        .expect(200)
        .expect("Content-Type", /json/);

      const movies = response.body;
      expect(movies).to.be.an("array");
      expect(movies).length.to.be.greaterThan(0);
    });

    it("should have valid movies", async function () {
      const response = await request(app)
        .get("/movies")
        .expect(200)
        .expect("Content-Type", /json/);

      const movies = response.body;
      expect(movies).to.be.an("array");

      movies.forEach(movie => {
        expect(movie.name).to.be.a("string");
        expect(movie.year).to.be.a("number");
        expect(movie.rating).to.be.a("number");
        expect(movie.description).to.be.a("string");
        expect(movie.director).to.be.a("string");
        expect(movie.genres).to.be.an("array");
      });
    });
  });
});

Chai, as mentioned before, is an assertion library, and we use it here primarily for type checking the data coming back from the API. We use SuperTest to make API calls. We pass our app object to SuperTest, and it handles making sure the API is listening, and gives us access to some useful HTTP request builders that include assertion functions as well.

We import the expect function from Chai, and use it instead of the built-in Mocha ones. The tests read out like plain English, and are very easy to understand. Chai actually has several interfaces that allow you to write tests such as "should" and "assert". These are detailed on their website.

Hooks

Imagine that the API we're testing has a persistent database of some kind. The data stored in these database will naturally persist between tests, and this can be bad if we want tests to be reproducible. To solve this problem, we can use hooks. Hooks allow code to be run before and after test blocks.

In this case we want to use them to initialize and destroy the database the API will be using for each test to ensure the state is consistent every time a test is run, and will always have the same result.

Here is an example of the beforeEach and afterEach hooks in action.

const chai = require("chai");
const request = require("supertest");

const expect = chai.expect;
const app = require("../../app");
const db = require("../../db");

describe("Movies", function () {
  // Create the schema before each test in the describe block.
  beforeEach(async function () {
    const database = await db.database;
    await db.initDB(database);
  });

  // Drop the schema before each test in the describe block.
  afterEach(async function () {
    const database = await db.database;
    await db.resetDB(database);
  });

  describe("GET /movies", function () {
    it("should return 200 OK with several movies", async function () {
      ...
    });

    it("should have valid movies", async function () {
      ...
    });
  });

  describe("GET /movies/:id", function () {
    it("should query an individual movie", async function () {
      ...
    });
  });

  describe("POST /movies", function () {
    it("should create a new movie", async function () {
      ...
    });
  });

  describe("PATCH /movies/:id", function () {
    it("should update an existing movie", async function () {
      ...
    });
  });

  describe("DELETE /movies/:id", function () {
    it("should delete an existing movie", async function () {
      ...
    });
  });
});

I've removed the contents of the actual tests since they're a bit long, but they're in the GitHub repository if you're curious. The details of the db and app modules are outside the context of this article.

The main takeaway here is the code inside of the beforeEach hook will tell Mocha to create the database before each test, and the code in the afterEach hook will tell Mocha to destroy the database after each test. I should also mention it and describe are also hooks as well!

Mocha Hooks:

it: Defines a single test.
describe: Defines a block of tests.
before: Run only once before the first test in the describe block.
after: Run only once after the last test in the describe block.
beforeEach: Runs before every test in the describe block.
afterEach: Runs after every test in the describe block.

Conclusion

Effective use of testing libraries in Node.js like Mocha and Chai can help make it easy to write tests and make your application more reliable. The example projects used throughout this article can be found on my GitHub. I have only scratched the surface here when it comes to testing. There are also frontend testing frameworks such as Selenium and Cypress. We've actually written about Cypress on our blog as well, and you can find that here if you're curious.

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

About the author(s)

Jamie Kuppens
I’m a software engineer with an interest in web development and some more esoteric things like emulator development.
@Reshurum @Reshurum

Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. 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This is helpful as it reduces the burden on the developer when making their applications accessible by ensuring that user expectations concerning interaction are met, and it can also potentially simplify the implementation of dialogs in general. Basic usage Adding a dialog using the new tag can be achieved with just a few lines of code. ` However, adding the dialog alone won’t do anything to the page. It will show up only once you call the .showModal() method against it. ` Then if you want to close it you can call the .close() method on the dialog, or press the escape key to close it, just like most other modals work. Also, note how a backdrop appears that darkens the rest of the page and prevents you from interacting with it. Neat! Accessibility and focus management Correctly handling focus is important when making your web applications accessible to all users. Typically you have to move the current focus to the active dialog when showing them, but with the dialog element that’s done for you. By default, the focus will be set on the first focusable element in the dialog. You can optionally change which element receives focus first by setting the autofocus attribute on the element you want the focus to start on, as seen in the previous example where that attribute was added to the close element. Using the .showModal() method to open the dialog also implicitly adds the dialog ARIA role to the dialog element. This helps screen readers understand that a modal has appeared and the screen so it can act accordingly. Adding forms to dialogs Forms can also be added to dialogs, and there’s even a special method value for them. If you add a element with the method set to dialog then the form will have some different behaviors that differ from the standard get and post form methods. First off, no external HTTP request will be made with this new method. What will happen instead is that when the form gets submitted, the returnValue property on the form element will be set to the value of the submit button in the form. So given this example form: ` The form element with the example-form id will have its returnValue set to Submit. In addition to that, the dialog will close immediately after the submit event is done being handled, though not before automatic form validation is done. If this fails then the invalid event will be emitted. You may have already noticed one caveat to all of this. You might not want the form to close automatically when the submit handler is done running. If you perform an asynchronous request with an API or server you may want to wait for a response and show any errors that occur before dismissing the dialog. 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That’s the opposite of what we want to do. Unfortunately, you cannot listen for a click event on the backdrop as it is considered to be part of the dialog itself. Adding this event listener by itself will effectively make clicking anywhere on the page dismiss the dialog. To correct for this we need to wrap the contents of the dialog content with another element that will effectively mask the dialog and receive the click instead. A simple element can do! ` Even this isn’t perfect though as the contents of the div may have elements with margins in them that will push the div down, resulting in clicks close to the edges of the dialog to dismiss it. This can be resolved by adding a couple of styles the the wrapping div that will make the margin stay contained within the wrapper element. 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Aug 2, 2024

4 mins

JavaScript

"How do I undo my most recent commit?" - Mastering the git reset command

"How do I undo my most recent commit?" - Mastering the git reset command Git is a powerful version control system, but even experienced developers sometimes make mistakes. Whether you've committed changes to the wrong branch, made a typo in your commit message, or simply want to undo recent changes, the git reset command is your go-to solution. In this post, we'll explore how to use git reset to manage your commit history effectively. The Basics of git reset At its core, git reset moves the HEAD and current branch pointer to a specified commit, effectively "resetting" your working directory to a previous state. The basic syntax is: ` Where specifies how to handle changes in the working directory and staging area, and is the commit you want to reset to. Soft vs. Hard Reset: Understanding the Difference Git reset has two main modes: --soft and --hard. Let's explore each: Soft Reset (--soft) A soft reset moves your HEAD to the specified commit but keeps your changes staged. This is useful when you want to undo commits but keep the changes for recommitting. Example: ` This command undoes the last commit, keeping the changes staged. You can now make additional changes or recommit with a new message. Hard Reset (--hard) A hard reset is more drastic. It moves your HEAD to the specified commit and discards all changes after that commit. Example: ` This command undoes the last commit and discards all changes. Be cautious with this command, as it can lead to data loss! Specifying a Commit Hash Instead of using relative references like HEAD~1, you can specify an exact commit hash: ` This resets to the commit with hash 1a2b3c4, keeping changes staged. Using HEAD~n Notation The HEAD~n notation allows you to specify how many commits back you want to reset. For example: ` This resets to three commits before the current HEAD, discarding all changes. Reverting Pushed Changes If you've already pushed commits you want to undo, you should use git revert instead of git reset to avoid rewriting public history. However, if you must use git reset on a shared branch, you'll need to force push: ` Be extremely cautious with force pushing, as it can cause issues for other developers. Generic Usage of git reset git reset can also be used to unstage changes: ` This removes file.txt from the staging area without changing its contents. Conclusion The git reset command is a powerful tool for managing your Git history. Whether you're undoing recent commits, unstaging changes, or completely resetting your working directory, understanding the different modes of git reset can help you navigate tricky situations in your Git workflow. Remember to use git reset with caution, especially when working with shared repositories. When in doubt, create a backup branch before performing any reset operations....

Oct 18, 2024

2 mins

Git

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Introduction to Testing in Node.js with Mocha and Chai

The Purpose of Testing

What are Mocha and Chai?

Installing Mocha and Friends

The Basics

Let's test an API

Hooks

Conclusion

Jamie Kuppens

You might also like

Introduction to Zod for Data Validation

The HTML Dialog Element: Enhancing Accessibility and Ease of Use

Making Seamless Page Transitions with the View Transitions API

"How do I undo my most recent commit?" - Mastering the git reset command

Let's innovate together!

You might also like

Introduction to Zod for Data Validation

The HTML Dialog Element: Enhancing Accessibility and Ease of Use

Making Seamless Page Transitions with the View Transitions API

"How do I undo my most recent commit?" - Mastering the git reset command