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LoopBack 4 extensions are often used by other teams. A thorough test suite for your extension brings powerful benefits to all your users, including:

  • Validating the behavior of the extension
  • Preventing unwanted changes to the API or functionality of the extension
  • Providing working samples and code snippets that serve as functional documentation for your users

Project Setup

We recommend that you use @loopback/cli to create the extension, as it installs several tools you can use for testing, such as mocha, assertion libraries, linters, etc.

The @loopback/cli includes the mocha automated test runner and a test folder containing recommended folders for various types of tests. Mocha is enabled by default if @loopback/cli is used to create the extension project. The @loopback/cli installs and configures mocha, creates the test folder, and also enters a test command in your package.json.

Assertion libraries such as ShouldJS (as expect), SinonJS, and a test sandbox are made available through the convenient @loopback/testlab package. The testlab is also installed by @loopback/cli.

Manual Setup - Using Mocha

  • Install mocha by running npm i --save-dev mocha. This will save the mocha package in package.json as well.
  • Under scripts in package.json add the following: test: npm run build && mocha --recursive ./dist/test

Types of tests

A comprehensive test suite tests many aspects of your code. We recommend that you write unit, integration, and acceptance tests to test your application from a variety of perspectives. Comprehensive testing ensures correctness, integration, and future compatibility.

You may use any development methodology you want to write your extension; the important thing is to test it with an automated test suite. In Traditional development methodology, you write the code first and then write the tests. In Test-driven development methodology, you write the tests first, see them fail, then write the code to pass the tests.

Unit Tests

A unit test tests the smallest unit of code possible, which in this case is a function. Unit tests ensure variable and state changes by outside actors don’t affect the results. Test doubles should be used to substitute function dependencies. You can learn more about test doubles and Unit testing here: Testing your Application: Unit testing.


At its core, a controller is a simple class that is responsible for related actions on an object. Performing unit tests on a controller in an extension is the same as performing unit tests on a controller in an application.

To test a controller, you instantiate a new instance of your controller class and test a function, providing a test double for constructor arguments as needed. Following are examples that illustrate how to perform a unit test on a controller class:


export class PingController {
  ping(msg?: string) {
    return `You pinged with ${msg}`;


import {PingController} from '../../..';
import {expect} from '@loopback/testlab';

describe('PingController() unit', () => {
  it('pings with no input', () => {
    const controller = new PingController();
    const result =;
    expect(result).to.equal('You pinged with undefined');

  it("pings with msg 'hello'", () => {
    const controller = new PingController();
    const result ='hello');
    expect(result).to.equal('You pinged with hello');

You can find an advanced example on testing controllers in Unit test your Controllers.


The recommended usage of a decorator is to store metadata about a class or a class method. The decorator implementation usually provides a function to retrieve the related metadata based on the class name and method name. For a unit test for a decorator, it is important to test that that it stores and retrieves the correct metadata. The retrieval gets tested as a result of validating whether the metadata was stored or not.

Following is an example for testing a decorator:


export function test(file: string) {
  return function (target: Object, methodName: string): void {

export function getTestMetadata(
  controllerClass: Constructor<{}>,
  methodName: string,
): {file: string} {
  return Reflector.getMetadata(


import {test, getTestMetadata} from '../../..';
import {expect} from '@loopback/testlab';

describe('test.decorator (unit)', () => {
  it('can store test name via a decorator', () => {
    class TestClass {
      me() {}

    const metadata = getTestMetadata(TestClass, 'me');


A Mixin is a TypeScript function that extends the Application Class, adding new constructor properties, methods, etc. It is difficult to write a unit test for a Mixin without the Application Class dependency. The recommended practice is to write an integration test is described in Mixin Integration Tests.


A Provider is a Class that implements the Provider interface. This interface requires the Class to have a value() function. A unit test for a provider should test the value() function by instantiating a new Provider class, using a test double for any constructor arguments.


import {Provider} from '@loopback/core';

export class RandomNumberProvider implements Provider<number> {
  value() {
    return (max: number): number => {
      return Math.floor(Math.random() * max) + 1;


import {RandomNumberProvider} from '../../..';
import {expect} from '@loopback/testlab';

describe('RandomNumberProvider (unit)', () => {
  it('generates a random number within range', () => {
    const provider = new RandomNumberProvider().value();
    const random: number = provider(3);

    expect(random).to.equalOneOf([1, 2, 3]);


This section will be provided in a future version.

Integration Tests

An integration test plays an important part in your test suite by ensuring your extension artifacts work together as well as @loopback. It is recommended to test two items together and substitute other integrations as test doubles so it becomes apparent where the integration errors may occur.

Mixin Integration Tests

A Mixin extends a base Class by returning an anonymous class. Thus, a Mixin is tested by actually using the Mixin with its base Class. Since this requires two Classes to work together, an integration test is needed. A Mixin test checks that new or overridden methods exist and work as expected in the new Mixed class. Following is an example for an integration test for a Mixin:


import {Constructor} from '@loopback/core';
export function TimeMixin<T extends MixinTarget<object>>(superClass: T) {
  return class extends superClass {
    constructor(...args: any[]) {
      if (!this.options) this.options = {};

      if (typeof this.options.timeAsString !== 'boolean') {
        this.options.timeAsString = false;

    time() {
      if (this.options.timeAsString) {
        return new Date().toString();
      return new Date();


import {expect} from '@loopback/testlab';
import {Application} from '@loopback/core';
import {TimeMixin} from '../../..';

describe('TimeMixin (integration)', () => {
  it('mixed class has .time()', () => {
    const myApp = new AppWithTime();
    expect(typeof myApp.time)'function');

  it('returns time as string', () => {
    const myApp = new AppWithLogLevel({
      timeAsString: true,

    const time = myApp.time();

  it('returns time as Date', () => {
    const myApp = new AppWithLogLevel();

    const time = myApp.time();

  class AppWithTime extends TimeMixin(Application) {}

Acceptance Test

An Acceptance test for an extension is a comprehensive test written end-to-end. Acceptance tests cover the user scenarios. An acceptance test uses all of the extension artifacts such as decorators, mixins, providers, repositories, etc. No test doubles are needed for an Acceptance test. This is a black box test where you don’t know or care about the internals of the extensions. You will be using the extension as if you were the consumer.

Due to the complexity of an Acceptance test, there is no example given here. Have a look at loopback4-example-log-extension to understand the extension artifacts and their usage. An Acceptance test can be seen here: src/tests/acceptance/log.extension.acceptance.ts.