faker VS SimpleCov

While we’re at it, let's add a couple of other gems we’ll need for our test environment: factory_bot_rails is a fixtures replacement and generates test model instances. faker is handy for generating fake strings of data to be used in tests. Add those gems to the development and test group of your Gemfile:

Next up, we'll combine a few of the techniques we've reviewed to implement snappy typeahead searching. Before we do that, though, let's generate more sample data. I will use the popular faker gem to do that:

Faker was originally written in Perl and is also available as a library for Ruby, Java, and Python.

Faker

rspec-rails factory_bot_rails faker

Ah, so the data is just a giant .yml file of potential options, that I compiled, and then faker grabs a random one based on that data. There's no "database" or anything akin to that.

It's with the Faker library. I was able to reproduce it by spinning up a new rails up, specifying the faker gem , yours, and running the command gives the same error.

To begin using faker, you need to install the gem, which is simply done by running "gem install faker". After you do that, you should add "gem "faker"" to your gem file to ensure correct usage, otherwise you may get an error. Once thats done you can head over to the seeding file and add require "faker" the page. In the seeding file is where you would start to "fake" your data and we use the faker library to pick out random dummy data. In the seeding, your gonna start implementing the data which should look like this,

gem "simplecov" - https://github.com/simplecov-ruby/simplecov | Gather spec coverage stats locally and on CI, aim for those 90+%.

Have you wondered how much of the logic in your views is exercised in your test suite? Thanks to this change, now you can see that in tools like SimpleCov.

My talk is about different best practices - specifically when adhering to them breaks down. One of those best practices is high test coverage. I start to work on the content for my presentation by building the code samples that I want to use in the slides. For the code coverage section, I'm writing some code with some tests. I'm using SimpleCov to generate code coverage results.

SimpleCov - a tool to check Ruby application code coverage. You can configure it to run alongside your tests. It provides metrics on code coverage so that you can identify what you need to pay attention to and where to invest your time to create better test cases.

Ensure that you have sufficient test coverage. You can use code coverage analysis tools like SimpleCov to gain insight into gaps in your coverage.

simplecov might the answer you need, it generates a report of the lines of code your test suite hits.

Use SimpleCov to generate a report of how many statements are covered by your test suite. It won't assess the test suite quality, though.

There are many tools for measuring test coverage, but one is SimpleCov. It also supports branches coverage. To measure coverage of production code, check out Coverband, which you can set up to use oneshot lines mode.