Jasmine Data Driven Tests

This plugin for Jasmine 2.x allows you to easily create data driven tests.

Features include:

• Automatically creates a Suite for each data driven test, making it easy to run all variants of a test in the test runner.
• Automatically creates one spec for each variant of the test, making it easy to run a single variant of a test in the test runner.
• The dataset for the test is just a hard coded Array, making the data easy to create and maintain.
• One or more arguments can be passed to the test function
• Asynchronous Jasmine specs are supported
• The this variable works the same as it does for regular Jasmine specs
• The dataset is inspected before creating the tests to ensure your specs do not act wonky because you expect 3 arguments, but one of your variants only has 2.
• If the dataset is not well formed, informative errors are thrown to make fixing issues with writing data driven tests easier to debug.

Installation:

1. Download package:

npm install jasmine-data_driven_tests

1. Inject it to karma.conf.js

files: [
  'node_modules/jasmine-data_driven_tests/src/all.js',
  ...
],

Getting Started

Simply include src/all.js after the source files for Jasmine. Now, you have two global functions available to you:

Data Driven Tests:

all(description, dataset, callback);
using(description, dataset, callback);

Data Driven Tests, marked as pending:

xall(description, dataset, callback);
xusing(description, dataset, callback);

Data Driven Test Basics

Data Driven Tests have three basic components:

1. The description
2. The dataset, which is an array of arguments passed to the spec function
3. The spec function

A quick example:

all("blank values are invalid",
    [
        "",
        null,
        undefined
    ],
    function(value) {
        expect(isEmpty(value)).toBe(true);
    }
);

The call to all above is equivalent to these native Jasmine method calls:

describe("blank values are invalid", function() {
 
    it('blank values are invalid (Variant #0 <"">)', function() {
        expect(isEmpty("")).toBe(true);
    });
 
    it('blank values are invalid (Variant #1 <null>)', function() {
        expect(isEmpty(null)).toBe(true);
    });
 
    it('blank values are invalid (Variant #2 <undefined>)', function() {
        expect(isEmpty(undefined)).toBe(true);
    });
 
});

In the Jasmine test runner, you'll see the following output:

blank values are invalid

    blank values are invalid (Variant #0 <"">)
    blank values are invalid (Variant #1 <null>)
    blank values are invalid (Variant #2 <undefined>)

Since they are just regular describe's and it's, you can click on blank values are invalid to run every test case, or click on an individual variant to just run that one case.

While the all method expands to the jasmine it method, the using method expands to the describe method. The using method is used to create more complex data driven tests.

using("customer object initialization",
    [
        "fred",
        "barney"
    ],
    function(name) {
        var customer;
 
        beforeEach(function() {
            customer = new Customer(name);
        });
 
        it('should have active status', function () {
            expect(customer.isActive).toBe(true);
        });
 
        it('should have upper case name', function () {
            expect(isUpperCase(customer.name)).toBe(true);
        });
    }
);

The call to using above is equivalent to these native Jasmine method calls:

describe("customer object initialization", function() {
 
    describe('customer object initiation (Variant #0, <"fred">)', function() {
        var customer;
 
        beforeEach(function() {
            customer = new Customer("fred");
        });
 
        it('should have active status', function () {
            expect(customer.isActive).toBe(true);
        });
 
        it('should have upper case name', function () {
            expect(isUpperCase(customer.name)).toBe(true);
        });
    });
 
    describe('customer object initiation (Variant #1, <"barney">)', function() {
        var customer;
 
        beforeEach(function() {
            customer = new Customer("barney");
        });
 
        it('should have active status', function () {
            expect(customer.isActive).toBe(true);
        });
 
        it('should have upper case name', function () {
            expect(isUpperCase(customer.name)).toBe(true);
        });
    });
 
});

And for the icing on the cake? A using block can contain one or more all's:

using("numbers greater than zero",
    [
        1,
        2
    ],
    function(n) {
        all("negative numbers are less",
            [
                -1,
                -2
            ],
            function(n2) {
                expect(n2 < n1).toBe(true);
            }
        );
    }
);

Intermingling using with all produces these native Jasmine calls:

describe("numbers greater than zero", function() {
 
    describe("numbers greater than zero (Variant #0 <1>)", function() {
 
        describe("negative numbers are less", function() {
            it("negative numbers are less (Variant #0 <-1>)", function() {
                expect(-1 < 1).toBe(true);
            });
 
            it("negative numbers are less (Variant #1 <-2>)", function() {
                expect(-2 < 1).toBe(true);
            });
        });
 
    });
 
    describe("numbers greater than zero (Variant #1 <2>)", function() {
 
        describe("negative numbers are less", function() {
            it("negative numbers are less (Variant #0 <-1>)", function() {
                expect(-1 < 2).toBe(true);
            });
 
            it("negative numbers are less (Variant #1 <-2>)", function() {
                expect(-2 < 2).toBe(true);
            });
        });
 
    });
 
});

Unlimited Numbers of Arguments

You can pass as many arguments to your spec function as you want:

all("values are greater than 0",
    [
        [ 3, 1 ],
        [ 5, 2 ]
    ],
    function(a, b) {
        expect(a - b > 0).toBe(true);
    }
);

You'll see this in the test runner:

values are greater than 0

    values are greater than 0 (Variant #0 <3, 1>)
    values are greater than 0 (Variant #1 <5, 2>)

The same holds true for the using function as well.

Support for Asynchronous Specs

Asynchronous specs are also supported as long as your callback function accepts one more argument than your dataset provides.

In the following example, the dataset provides two arguments, and the callback function accepts three. The third argument is the done callback in Jasmine, which when called will complete the current spec and advance the test runner to the next one.

all("values are greater than 5",
    [
        [ 3, 1 ],
        [ 5, 2 ]
    ],
    function(a, b, done) {
        setTimeout(function() {
            expect(a - b > 0).toBe(true);
            done();
        }, 50);
    }
);

The all and xall functions are really just wrappers for describe, it, and xit. Data Driven Specs are supported anywhere Jasmine is supported.

Using this In Your Data Driven Tests

You can use the this keyword in your data driven tests just like you can with regular it specs:

beforeEach(function() {
    this.a = 5;
});
 
all("references to 'this' work",
    [ 1, 2, 3 ],
    function(b) {
        expect(this.a - b > 0).toBe(true);
    }
);

When To Use Data Driven Tests

You can really clean up your repetitive specs, but you should follow some guidelines to ensure accurate and readable tests.

• Expectations should be the same for each variant. Logic changing expectations should be avoided.
• Only the input to your test cases change. The expected output does not.
• The numbers of arguments passed to the spec function should be the same for each variant of the test.
• Try to use primative data types in all of your dataset arguments. If you need to use Objects or Array's consider breaking your test cases down even further so that you can use primative data types, though this is not a steadfast rule.
• Avoid custom logic when creating the dataset. Just simple, hard coded values will work best and avoid Code Smells.

Feature Requests and Bug Reports

No further feature enhancements are planned, however suggestions are always welcome. Just open a new Issue on GitHub explaining the feature request, and the use case for it.

Bug reports should also be managed at GitHub in the Issue Tracker for this repository.