Overview

A thorough automated test suite is important because it:

• Ensures your application works as expected.
• Prevents regressions when new features are added and bugs are fixed.
• Helps new and existing developers understand different parts of the codebase (knowledge sharing).
• Speeds up development over the long run (the code writes itself!).

Types of tests

We encourage writing tests from a few perspectives, mainly black-box testing (acceptance) and white-box testing (integration and unit). Tests are usually written using typical patterns such as arrange/act/assert or given/when/then. Both styles work well, so pick one that you’re comfortable with and start writing tests!

For an introduction to automated testing, see Define your testing strategy.

This article is a reference guide for common types of tests and test helpers.

Project setup

An automated test suite requires a test runner to execute all the tests and produce a summary report. We use and recommend Mocha.

In addition to a test runner, the test suites generally require:

• An assertion library (we recommend Should.js).
• A library for making HTTP calls and verifying their results (we recommend supertest).
• A library for creating test doubles (we recommend Sinon.JS).

The @loopback/testlab module integrates these packages and makes them easy to use together with LoopBack.

Set up testing infrastructure with LoopBack CLI

LoopBack applications that have been generated using the lb4 app command from @loopback/cli come with @loopback/testlab and mocha as a default, so no other testing infrastructure setup is needed.

Setup testing infrastructure manually

If you have an existing application install mocha and @loopback/testlab:

npm install --save-dev mocha @loopback/testlab

Your package.json should then look something like this:

{
  // ...
  "devDependencies": {
    "@loopback/testlab": "^<current-version>",
    "@types/mocha": "^<current-version>",
    "mocha": "^<current-version>"
  },
  "scripts": {
    "test": "mocha --recursive \"dist/test\""
  }
  // ...
}

Data handling

Tests accessing a real database often require existing data. For example, a method listing all products needs some products in the database; a method to create a new product instance must determine which properties are required and any restrictions on their values. There are various approaches to address this issue. Many of them unfortunately make the test suite difficult to understand, difficult to maintain, and prone to test failures unrelated to the changes made.

Our approach to data handling, based on our experience, is described in this section.

Create a test datasource

As we would prefer full control of the database and even the development database is not something we want to clean before each test it’s handy to use an independent in-memory datasource which is filled appropriately using test data builders before each test run.

import {juggler} from '@loopback/repository';

export const testdb: juggler.DataSource = new juggler.DataSource({
  name: 'db',
  connector: 'memory',
});

Clean the database before each test

Start with a clean database before each test. This may seem counter-intuitive: why not reset the database after the test has finished? When a test fails and the database is cleaned after the test has finished, then it’s difficult to observe what was stored in the database and why the test failed. When the database is cleaned in the beginning, then any failing test will leave the database in the state that caused the test to fail.

To clean the database before each test, set up a beforeEach hook to call a helper method; for example:

import {ProductRepository, CategoryRepository} from '../../repositories';
import {testdb} from '../fixtures/datasources/testdb.datasource';

export async function givenEmptyDatabase() {
  await new ProductRepository(testdb).deleteAll();
  await new CategoryRepository(testdb).deleteAll();
}

In case a repository includes a relation to another repository, ie. Product belongs to Category, include it in the repository call, for example:

import {Getter} from '@loopback/core';
import {ProductRepository, CategoryRepository} from '../../repositories';
import {testdb} from '../fixtures/datasources/testdb.datasource';

export async function givenEmptyDatabase() {
  let categoryRepository: CategoryRepository;
  let productRepository: ProductRepository;

  categoryRepository = new CategoryRepository(
    testdb,
    async () => productRepository,
  );

  productRepository = new ProductRepository(
    testdb,
    async () => categoryRepository,
  );

  await productRepository.deleteAll();
  await categoryRepository.deleteAll();
}

// in your test file
import {givenEmptyDatabase} from '../../helpers/database.helpers';

describe('ProductController (integration)', () => {
  before(givenEmptyDatabase);
  // etc.
});

Use test data builders

To avoid duplicating code for creating model data that is complete with required properties, use shared test data builders. This enables tests to provide the small subset of properties that is strictly required by the tested scenario. Using shared test builders will help your tests to be:

• Easier to understand, since it’s immediately clear what model properties are relevant to the tests. If the tests set the required properties, it is difficult to tell whether the properties are actually relevant to the tested scenario.

• Easier to maintain. As your data model evolves, you will need to add more required properties. If the tests build the model instance data manually, all the tests must be manually updated to set a new required property. With a shared test data builder, you update a single location with the new property.

See @loopback/openapi-spec-builder for an example of how to apply this design pattern for building OpenAPI Spec documents.

In practice, a simple function that adds missing required properties is sufficient.

// ...
export function givenProductData(data?: Partial<Product>) {
  return Object.assign(
    {
      name: 'a-product-name',
      slug: 'a-product-slug',
      price: 1,
      description: 'a-product-description',
      available: true,
    },
    data,
  );
}

export async function givenProduct(data?: Partial<Product>) {
  return new ProductRepository(testdb).create(givenProductData(data));
}
// ...

Avoid sharing the same data for multiple tests

It’s tempting to define a small set of data to be shared by all tests. For example, in an e-commerce application, you might pre-populate the database with a few categories, some products, an admin user and a customer. This approach has several downsides:

• When trying to understand any individual test, it’s difficult to tell what part of the pre-populated data is essential for the test and what’s irrelevant. For example, in a test checking the method counting the number of products in a category using a pre-populated category “Stationery”, is it important that “Stationery” contains nested sub-categories or is that fact irrelevant? If it’s irrelevant, then what are the other tests that depend on it?

• As the application grows and new features are added, it’s easier to add more properties to existing model instances rather than create new instances using only the properties required by the new features. For example, when adding a category image, it’s easier to add image to an existing category “Stationery” and perhaps keep another category “Groceries” without any image, rather than creating two new categories “CategoryWithAnImage” and “CategoryMissingImage”. This further amplifies the previous problem, because it’s not clear that “Groceries” is the category that should be used by tests requiring a category with no image - the category name does not provide any hints on that.

• As the shared dataset grows (together with the application), the time required to bring the database into its initial state grows too. Instead of running a few “DELETE ALL” queries before each test (which is relatively fast), you may have to run tens or hundreds of different commands used to create different model instances, thus triggering slow index rebuilds along the way and slowing down the test suite considerably.

Use the test data builders described in the previous section to populate your database with the data specific to your test only.

Write higher-level helpers to share the code for re-creating common scenarios. For example, if your application has two kinds of users (admins and customers), then you may write the following helpers to simplify writing acceptance tests checking access control:

async function givenAdminAndCustomer() {
  return {
    admin: await givenUser({role: Roles.ADMIN}),
    customer: await givenUser({role: Roles.CUSTOMER}),
  };
}

Unit testing

Unit tests are considered “white-box” tests because they use an “inside-out” approach where the tests know about the internals and control all the variables of the system being tested. Individual units are tested in isolation and their dependencies are replaced with Test doubles.

Use test doubles

Test doubles are functions or objects that look and behave like the real variants used in production, but are actually simplified versions that give the test more control of the behavior. For example, reproducing the situation where reading from a file failed because of a hard-drive error is pretty much impossible. However, using a test double to simulate the file-system API will provide control over what each call returns.

Sinon.JS has become the de-facto standard for test doubles in Node.js and JavaScript/TypeScript in general. The @loopback/testlab package comes with Sinon preconfigured with TypeScript type definitions and integrated with Should.js assertions.

There are three kinds of test doubles provided by Sinon.JS:

• Test spies are functions that record arguments, the return value, the value of this, and exceptions thrown (if any) for all its calls. There are two types of spies: Some are anonymous functions, while others wrap methods that already exist in the system under test.

• Test stubs are functions (spies) with pre-programmed behavior. As spies, stubs can be either anonymous, or wrap existing functions. When wrapping an existing function with a stub, the original function is not called.

• Test mocks (and mock expectations) are fake methods (like spies) with pre-programmed behavior (like stubs) as well as pre-programmed expectations. A mock will fail your test if it is not used as expected.

Create a stub Repository

When writing an application that accesses data in a database, the best practice is to use repositories to encapsulate all data-access/persistence-related code. Other parts of the application (typically controllers) can then depend on these repositories for data access. To test Repository dependents (for example, Controllers) in isolation, we need to provide a test double, usually as a test stub.

In traditional object-oriented languages like Java or C#, to enable unit tests to provide a custom implementation of the repository API, the controller needs to depend on an interface describing the API, and the repository implementation needs to implement this interface. The situation is easier in JavaScript and TypeScript. Thanks to the dynamic nature of the language, it’s possible to mock/stub entire classes.

Creating a test double for a repository class is very easy using the Sinon.JS utility function createStubInstance. It’s important to create a new stub instance for each unit test in order to prevent unintended re-use of pre-programmed behavior between (unrelated) tests.

describe('ProductController', () => {
  let repository: ProductRepository;
  beforeEach(givenStubbedRepository);

  // your unit tests

  function givenStubbedRepository() {
    repository = sinon.createStubInstance(ProductRepository);
  }
});

In your unit tests, you will usually want to program the behavior of stubbed methods (what they should return) and then verify that the Controller (unit under test) called the right method with the correct arguments.

Configure stub’s behavior at the beginning of your unit test (in the “arrange” or “given” section):

// repository.find() will return a promise that
// will be resolved with the provided array
const findStub = repository.find as sinon.SinonStub;
findStub.resolves([{id: 1, name: 'Pen'}]);

Verify how was the stubbed method executed at the end of your unit test (in the “assert” or “then” section):

// expect that repository.find() was called with the first
// argument deeply-equal to the provided object
sinon.assert.calledWithMatch(findStub, {where: {id: 1}});

See Unit test your controllers for a full example.

Create a stub Service

If your controller relies on service proxy for service oriented backends such as REST APIs, SOAP Web Services, or gRPC microservices, then we can create stubs for the service akin to the steps outlined in the above stub repository section. Consider a dependency on a GeoCoder service that relies on a remote REST API for returning coordinates for a specific address.

export interface GeocoderService {
  geocode(address: string): Promise<GeoPoint[]>;
}

The first step is to create a mocked instance of the GeocoderService API and configure its geocode method as a Sinon stub:

describe('GeocoderController', () => {
  let geoService: GeoCoderService;
  let geocode: sinon.SinonStub;

  beforeEach(givenMockGeoCoderService);

  // your unit tests

  function givenMockGeoCoderService() {
    // this creates a stub with GeocoderService API
    // in a way that allows the compiler to verify type correctness
    geoService = {geocode: sinon.stub()};

    // this creates a reference to the stubbed "geocode" method
    // because "geoService.geocode" has type from GeocoderService
    // and does not provide Sinon APIs
    geocode = geoService.geocode as sinon.SinonStub;
  }
});

Afterwards, we can configure the geocode stub’s behaviour before the act phase of our test(s):

// geoService.geocode() will return a promise that
// will be resolved with the provided array
geocode.resolves([<GeoPoint>{y: 41.109653, x: -73.72467}]);

Lastly, we’ll verify how the stub was executed:

// expect that geoService.geocode() was called with the first
// argument equal to the provided address string
sinon.assert.calledWithMatch(geocode, '1 New Orchard Road, Armonk, 10504');

Check out TodoController unit tests illustrating the above points in action for more information.

Unit test your Controllers

Unit tests should apply to the smallest piece of code possible to ensure that other variables and state changes do not pollute the result. A typical unit test creates a controller instance with dependencies replaced by test doubles and directly calls the tested method. The example below gives the controller a stub implementation of its repository dependency using the testlab createStubInstance function, ensures the controller calls the repository’s find() method with a correct query, and returns back the query results. See Create a stub repository for a detailed explanation.

import {
  createStubInstance,
  expect,
  sinon,
  StubbedInstanceWithSinonAccessor,
} from '@loopback/testlab';
import {ProductRepository} from '../../../repositories';
import {ProductController} from '../../../controllers';

describe('ProductController (unit)', () => {
  let repository: StubbedInstanceWithSinonAccessor<ProductRepository>;
  beforeEach(givenStubbedRepository);

  describe('getDetails()', () => {
    it('retrieves details of a product', async () => {
      const controller = new ProductController(repository);
      repository.stubs.find.resolves([{name: 'Pen', slug: 'pen'}]);

      const details = await controller.getDetails('pen');

      expect(details).to.containEql({name: 'Pen', slug: 'pen'});
      sinon.assert.calledWithMatch(repository.stubs.find, {
        where: {slug: 'pen'},
      });
    });
  });

  function givenStubbedRepository() {
    repository = createStubInstance(ProductRepository);
  }
});

Unit test your models and repositories

In a typical LoopBack application, models and repositories rely on behavior provided by the framework (@loopback/repository package) and there is no need to test LoopBack’s built-in functionality. However, any additional application-specific APIs do need new unit tests.

For example, if the Person Model has properties firstname, middlename and surname and provides a function to obtain the full name, then you should write unit tests to verify the implementation of this additional method.

Remember to use Test data builders whenever you need valid data to create a new model instance.

import {Person} from '../../../models';
import {givenPersonData} from '../../helpers/database.helpers';
import {expect} from '@loopback/testlab';

describe('Person (unit)', () => {
  // we recommend to group tests by method names
  describe('getFullName()', () => {
    it('uses all three parts when present', () => {
      const person = givenPerson({
        firstname: 'Jane',
        middlename: 'Smith',
        surname: 'Brown',
      });

      const fullName = person.getFullName();
      expect(fullName).to.equal('Jane Smith Brown');
    });

    it('omits middlename when not present', () => {
      const person = givenPerson({
        firstname: 'Mark',
        surname: 'Twain',
      });

      const fullName = person.getFullName();
      expect(fullName).to.equal('Mark Twain');
    });
  });

  function givenPerson(data: Partial<Person>) {
    return new Person(givenPersonData(data));
  }
});

Writing a unit test for custom repository methods is not as straightforward because CrudRepository is based on loopback-datasource-juggler which was not designed with dependency injection in mind. Instead, use integration tests to verify the implementation of custom repository methods. For more information, refer to Test your repositories against a real database in Integration Testing.

Unit test your Sequence

While it’s possible to test a custom Sequence class in isolation, it’s better to rely on acceptance-level tests in this exceptional case. The reason is that a custom Sequence class typically has many dependencies (which can make test setup long and complex), and at the same time it provides very little functionality on top of the injected sequence actions. Bugs are much more likely to be caused by the way the real sequence action implementations interact together (which is not covered by unit tests), instead of the Sequence code itself (which is the only thing covered).

See Test Sequence customizations in Acceptance Testing.

Integration testing

Integration tests are considered “white-box” tests because they use an “inside-out” approach that tests how multiple units work together or with external services. You can use test doubles to isolate tested units from external variables/state that are not part of the tested scenario.

Test your repositories against a real database

There are two common reasons for adding repository tests:

• Your models are using an advanced configuration, for example, custom column mappings, and you want to verify this configuration is correctly picked up by the framework.
• Your repositories have additional methods.

Integration tests are one of the places to put the best practices in Data handling to work:

• Clean the database before each test
• Use test data builders
• Avoid sharing the same data for multiple tests

Here is an example showing how to write an integration test for a custom repository method findByName:

import {
  givenEmptyDatabase,
  givenCategory,
} from '../../helpers/database.helpers';
import {CategoryRepository} from '../../../repositories';
import {expect} from '@loopback/testlab';
import {testdb} from '../../fixtures/datasources/testdb.datasource';

describe('CategoryRepository (integration)', () => {
  beforeEach(givenEmptyDatabase);

  describe('findByName(name)', () => {
    it('return the correct category', async () => {
      const stationery = await givenCategory({name: 'Stationery'});
      const repository = new CategoryRepository(testdb);

      const found = await repository.findByName('Stationery');

      expect(found).to.deepEqual(stationery);
    });
  });
});

Test controllers and repositories together

Integration tests running controllers with real repositories are important to verify that the controllers use the repository API correctly, and that the commands and queries produce expected results when executed on a real database. These tests are similar to repository tests with controllers added as another ingredient.

import {expect} from '@loopback/testlab';
import {givenEmptyDatabase, givenProduct} from '../../helpers/database.helpers';
import {ProductController} from '../../../controllers';
import {ProductRepository} from '../../../repositories';
import {testdb} from '../../fixtures/datasources/testdb.datasource';

describe('ProductController (integration)', () => {
  beforeEach(givenEmptyDatabase);

  describe('getDetails()', () => {
    it('retrieves details of the given product', async () => {
      const pencil = await givenProduct({name: 'Pencil', slug: 'pencil'});
      const controller = new ProductController(new ProductRepository(testdb));

      const details = await controller.getDetails('pencil');

      expect(details).to.containEql(pencil);
    });
  });
});

Test your Services against real backends

When integrating with other services (including our own microservices), it’s important to verify that our client configuration is correct and the client (service proxy) API is matching the actual service implementation. Ideally, there should be at least one integration test for each endpoint (operation) consumed by the application.

To write an integration test, we need to:

• Obtain an instance of the tested service proxy. Optionally modify the connection configuration, for example change the target URL or configure a caching proxy to speed up tests.
• Execute service proxy methods and verify that expected results were returned by the backend service.

Obtain a Service Proxy instance

In Make service proxies easier to test, we are suggesting to leverage Providers as a tool allowing both the IoC framework and the tests to access service proxy instances.

In the integration tests, a test helper should be written to obtain an instance of the service proxy by invoking the provider. This helper should be typically invoked once before the integration test suite begins.

import {GeoService, GeoServiceProvider} from '../../services/geo.service';
import {GeoDataSource} from '../../datasources/geo.datasource';

describe('GeoService', () => {
  let service: GeoService;
  before(givenGeoService);

  // to be done: add tests here

  function givenGeoService() {
    const dataSource = new GeoDataSource();
    service = new GeoServiceProvider(dataSource).value();
  }
});

If needed, you can tweak the datasource config before creating the service instance:

import {merge} from 'lodash';
import {GeocoderDataSource} from '../datasources/geocoder.datasource';

function givenGeoService() {
  const config = merge({}, GeocoderDataSource.defaultConfig, {
    // your config overrides
  });
  const dataSource = new GeoDataSource(config);
  service = new GeoServiceProvider(dataSource).value();
}

Test individual service methods

With the service proxy instance available, integration tests can focus on executing individual methods with the right set of input parameters; and verifying the outcome of those calls.

it('resolves an address to a geo point', async () => {
  const points = await service.geocode('1 New Orchard Road, Armonk, 10504');

  expect(points).to.deepEqual([
    {
      lat: 41.109653,
      lng: -73.72467,
    },
  ]);
});

Acceptance (end-to-end) testing

Automated acceptance (end-to-end) tests are considered “black-box” tests because they use an “outside-in” approach that is not concerned about the internals of the system. Acceptance tests perform the same actions (send the same HTTP requests) as the clients and consumers of your API will do, and verify that the results returned by the system match the expected results.

Typically, acceptance tests start the application, make HTTP requests to the server, and verify the returned response. LoopBack uses supertest to create test code that simplifies both the execution of HTTP requests and the verification of responses. Remember to follow the best practices from Data handling when setting up your database for tests:

• Clean the database before each test
• Use test data builders
• Avoid sharing the same data for multiple tests

Validate your OpenAPI specification

The OpenAPI specification is a cornerstone of applications that provide REST APIs. It enables API consumers to leverage a whole ecosystem of related tooling. To make the spec useful, you must ensure it’s a valid OpenAPI Spec document, ideally in an automated way that’s an integral part of regular CI builds. LoopBack’s testlab module provides a helper method validateApiSpec that builds on top of the popular swagger-parser package.

Example usage:

import {HelloWorldApplication} from '../..';
import {RestServer} from '@loopback/rest';
import {validateApiSpec} from '@loopback/testlab';

describe('API specification', () => {
  it('api spec is valid', async () => {
    const app = new HelloWorldApplication();
    const server = await app.getServer(RestServer);
    const spec = server.getApiSpec();
    await validateApiSpec(spec);
  });
});

Perform an auto-generated smoke test of your REST API

The formal validity of your application’s spec does not guarantee that your implementation is actually matching the specified behavior. To keep your spec in sync with your implementation, you should use an automated tool like Dredd to run a set of smoke tests to verify your app conforms to the spec.

Automated testing tools usually require hints in your specification to tell them how to create valid requests or what response data to expect. Dredd in particular relies on response examples and request parameter x-example fields. Extending your API spec with examples is a good thing on its own, since developers consuming your API will find them useful too.

Here is an example showing how to run Dredd to test your API against the spec:

import {expect} from '@loopback/testlab';
import {HelloWorldApplication} from '../..';
import {RestServer, RestBindings} from '@loopback/rest';
import {spec} from '../../apidefs/openapi';
const Dredd = require('dredd');

describe('API (acceptance)', () => {
  let app: HelloWorldApplication;
  /* eslint-disable @typescript-eslint/no-explicit-any */
  let dredd: any;
  before(initEnvironment);
  after(async () => {
    await app.stop();
  });

  it('conforms to the specification', done => {
    dredd.run((err: Error, stats: object) => {
      if (err) return done(err);
      expect(stats).to.containDeep({
        failures: 0,
        errors: 0,
        skipped: 0,
      });
      done();
    });
  });

  async function initEnvironment() {
    app = new HelloWorldApplication();
    const server = await app.getServer(RestServer);
    // For testing, we'll let the OS pick an available port by setting
    // RestBindings.PORT to 0.
    server.bind(RestBindings.PORT).to(0);
    // app.start() starts up the HTTP server and binds the acquired port
    // number to RestBindings.PORT.
    await app.boot();
    await app.start();
    // Get the real port number.
    const port = await server.get(RestBindings.PORT);
    const baseUrl = `http://localhost:${port}`;
    const config: object = {
      server: baseUrl, // base path to the end points
      options: {
        level: 'fail', // report 'fail' case only
        silent: false, // false for helpful debugging info
        path: [`${baseUrl}/openapi.json`], // to download apiSpec from the service
      },
    };
    dredd = new Dredd(config);
  }
});

The user experience needs improvement and we are looking into better solutions. See GitHub issue #644. Let us know if you have any recommendations!

Test your individual REST API endpoints

You should have at least one acceptance (end-to-end) test for each of your REST API endpoints. Consider adding more tests if your endpoint depends on (custom) sequence actions to modify the behavior when the corresponding controller method is invoked via REST, compared to behavior observed when the controller method is invoked directly via JavaScript/TypeScript API. For example, if your endpoint returns different responses to regular users and to admin users, then you should two tests (one test for each user role).

Here is an example of an acceptance test:

import {HelloWorldApplication} from '../..';
import {Client, createRestAppClient, expect} from '@loopback/testlab';
import {givenEmptyDatabase, givenProduct} from '../helpers/database.helpers';
import {RestServer, RestBindings} from '@loopback/rest';
import {testdb} from '../fixtures/datasources/testdb.datasource';

describe('Product (acceptance)', () => {
  let app: HelloWorldApplication;
  let client: Client;

  before(givenEmptyDatabase);
  before(givenRunningApp);
  after(async () => {
    await app.stop();
  });

  it('retrieves product details', async () => {
    // arrange
    const product = await givenProduct({
      name: 'Ink Pen',
      slug: 'ink-pen',
      price: 1,
      category: 'Stationery',
      description: 'The ultimate ink-powered pen for daily writing',
      label: 'popular',
      available: true,
      endDate: null,
    });
    const expected = Object.assign({id: product.id}, product);

    // act
    const response = await client.get('/product/ink-pen');

    // assert
    expect(response.body).to.containEql(expected);
  });

  async function givenRunningApp() {
    app = new HelloWorldApplication({
      rest: {
        port: 0,
      },
    });
    await app.boot();

    // change to use the test datasource after the app has been booted so that
    // it is not overridden
    app.dataSource(testdb);
    await app.start();

    client = createRestAppClient(app);
  }
});

Test Sequence customizations

Custom sequence behavior is best tested by observing changes in behavior of the affected endpoints. For example, if your sequence has an authentication step that rejects anonymous requests for certain endpoints, then you can write a test making an anonymous request to those endpoints to verify that it’s correctly rejected. These tests are essentially the same as the tests verifying implementation of individual endpoints as described in the previous section.

Code coverage

@loopback/build contains a command line tool (lb-nyc) that acts as a wrapper for nyc.

To set up code coverage:

• Create .nycrc in your project’s root directory

  {
    "include": ["dist"],
    "exclude": ["dist/__tests__/"],
    "extension": [".js", ".ts"],
    "reporter": ["text", "html"],
    "exclude-after-remap": false
  }
  

• Update your package.json scripts:

  "precoverage": "npm test",
  "coverage": "open coverage/index.html",
  "coverage:ci": "lb-nyc report --reporter=text-lcov | coveralls",
  "test": "lb-nyc npm run mocha",
  "test:ci": "lb-nyc npm run mocha"
  

  coverage:ci sets up integration with Coveralls.