Radagen is a psuedo random data generator library for the Ruby language built with two primary design goals: composition and sizing. These two properties allow this library to be used in a range of different applications from simple test data generation, model checking, fuzz testing, database seeding to the foundation of a generative/property based testing framework.
- Ruby 2.0+
Add this line to your application's Gemfile:
gem 'radagen'And then execute:
$ bundle
Or install it yourself as:
$ gem install radagen
The main use case for Radagen is to create data generators that produce arbitrarily complex values. These generators can then be used in many different contexts. Let's start with a few of the scalar generators provided by Radagen.
require 'radagen'
gen = Radagen
my_fixnum = gen.fixnum
my_string = gen.string_alphanumericSo far we required the Radagen gem and "namespaced" the Radagen module to gen. Throughout the rest of this documentation it will be assumed Radagen is namespaced to gen. Let's take look at what values these generators produce.
my_fixnum.sample => [0, 0, -1, 1, -3, -4, -5, -1, -2, -7]
my_string.sample => ["", "", "jV", "", "7zS", "2", "U9O84Q", "4S", "6Ccw66", "0Sip741V"]sample above is a utility method on the Radagen::Generator object that allows you to interact with your generator seeing what type of values it will produce. As shown above sample returns a sampling of 10 values by default. You can also provide a count.
my_string.sample(30) => ["",
"",
"Qo",
"T",
"X",
"qy10O",
"Vh",
"0omZ",
"l30fB",
"r08yruW",
"27q7zGR",
"6jSEk1r",
"k667",
"v9VUZYnn",
"M2K8Hd",
"",
"4Lu82vRMviY",
"LB",
"lB",
"H0aBry87ykl",
"",
"8JMjNC",
"Gr6lxA",
"",
"1VfvdCjA9t2PL72Xa",
"vI",
"lUabvF4Rg06RWl71V27fi53",
"qw2Uo71ADT",
"550EbA0UX9f9Sc4I",
"9QbchgbZtY7C57Eq"]Notice the values produced by my_string generator. They grow in size and complexity because as sample calls the generator, it passes a larger and larger size value. This is a very important aspect of all Radagen generators and will be detailed further in sizing.
scalar generators are interesting but can only take you so far. We now will explore the ideas around composition. Lets build on the previous generators.
my_hash = gen.hash(:fixnum => my_fixnum, :string => my_string)
my_hash.sample => [{:fixnum=>0, :string=>""},
{:fixnum=>1, :string=>"9"},
{:fixnum=>-1, :string=>"1"},
{:fixnum=>2, :string=>"2F2"},
{:fixnum=>1, :string=>""},
{:fixnum=>3, :string=>""},
{:fixnum=>5, :string=>""},
{:fixnum=>1, :string=>"4V9yx"},
{:fixnum=>8, :string=>""},
{:fixnum=>-1, :string=>"Dy443"}]Above we have built a hash generator which uses values taken from previous generators. There is of course little difference between the above and the following:
my_hash = gen.hash(:fixnum => gen.fixnum, :string => gen.string_alphanumeric)
my_hash.sample => [{:fixnum=>0, :string=>""},
{:fixnum=>0, :string=>""},
{:fixnum=>0, :string=>""},
{:fixnum=>2, :string=>"W9"},
{:fixnum=>-1, :string=>"79BG"},
{:fixnum=>2, :string=>"YEF0"},
{:fixnum=>0, :string=>"IQDe"},
{:fixnum=>-2, :string=>"mRo"},
{:fixnum=>-7, :string=>"F958K0"},
{:fixnum=>7, :string=>"18T"}]However the following becomes more interesting.
meta = gen.hash(:fixnum => gen.fixnum, :string => gen.string_alphanumeric)
individual_account = gen.hash(:account_id => gen.uuid, :type => gen.return('individual'), :meta => meta)
individual_account.sample => [{:account_id=>"d4f6a194-d2d8-4c4f-9a89-df3f477f3dfc", :type=>"individual", :meta=>{:fixnum=>0, :string=>""}},
{:account_id=>"e6da491f-0af3-4ab4-9529-00a5025cbbde", :type=>"individual", :meta=>{:fixnum=>-1, :string=>""}},
{:account_id=>"4c3a3ebb-0aa3-4fc7-9c66-022ef2c1b77a", :type=>"individual", :meta=>{:fixnum=>-1, :string=>""}},
{:account_id=>"2a21493e-5ea7-4ae4-b813-bfe7052c5ba0", :type=>"individual", :meta=>{:fixnum=>-2, :string=>""}},
{:account_id=>"54a06f43-35bf-4b86-af36-499164fdec0e", :type=>"individual", :meta=>{:fixnum=>2, :string=>"lYnP"}},
{:account_id=>"17d42c28-c9a2-484d-a54f-3063fba893a2",
:type=>"individual",
:meta=>{:fixnum=>-4, :string=>"92e8k"}},
{:account_id=>"c77a7dc6-7c13-4d68-b5ff-dd4d4d22366f", :type=>"individual", :meta=>{:fixnum=>4, :string=>"gEDq"}},
{:account_id=>"f0e2b910-70c7-4b65-9dd1-9e89ef03ec00",
:type=>"individual",
:meta=>{:fixnum=>3, :string=>"e2EYLzk"}},
{:account_id=>"7bce02de-6d77-4e4e-91c9-8f518cc73223",
:type=>"individual",
:meta=>{:fixnum=>4, :string=>"Qnh5Z"}},
{:account_id=>"8781bf21-85f0-40f4-b407-85805da60ba6", :type=>"individual", :meta=>{:fixnum=>-1, :string=>"Z"}}]The hash generator combinator conveniently will nest any generator. But what if you would like to make your own combinators? There are two primitives to work with; bind and fmap.
domain = gen.elements(['gmail.com', 'mailinator.com'])
name = gen.string_ascii
email_account = gen.fmap(gen.tuple(name, domain)) do |name, domain|
"#{name}@#{domain}"
end
email_account.sample => ["@gmail.com", "[email protected]", "[email protected]", "[email protected]", "z^@mailinator.com", "[email protected]", "(t-f;@gmail.com", "@gmail.com", ")[email protected]", "[email protected]"]elements will randomly select an element from the array you pass it (ex. 'gmail.com' or 'mailinator.com'). string_ascii will produce strings containing the ascii band of characters. fmap will take values from a generator, which in this case was a two tuple with the first value taken from the name generator and the second taken from the domain generator, and passes those values to a block. Within the block we do some destructuring to the tuple and with string interpolation we return an email account string. Note the block passed to fmap requires you return a value NOT another generator.
The first example you noticed has an empty name which isn't a valid email address. We see our first example of how a 'stocastic' like tool can challenge our assumptions, or at least forces you to consider the domain you are working a little deeper.
If you don't want the generator to produce empty names you could do the following:
name = gen.not_empty(gen.string_ascii)
name.sample => ["1", "G", "y", "K", "<xv", "|5`", "u4GgC", "^", "n(]yZ2", "V7-"]Using not_empty here takes the string_ascii generator, returning another generator that won't produce empty strings.
bind is similar to fmap but the block instead of returning a value needs to return a Radagen::Generator.
account = gen.hash({:id => gen.uuid, :name => gen.string_alpha, :comment_count => gen.fixnum_pos})
accounts = gen.not_empty(gen.array(account)) #an array of non-empty accounts
accounts_and_selection = gen.bind(accounts) do |accounts|
gen.tuple(gen.identity(accounts), gen.elements(accounts))
end
accounts_and_selection.sample => [[[{:id=>"8f9308be-976f-447b-b207-3e4f3391d8da", :name=>"f", :comment_count=>1}], {:id=>"8f9308be-976f-447b-b207-3e4f3391d8da", :name=>"f", :comment_count=>1}],
[[{:id=>"f28cde53-4f8f-4548-9192-57c3b44af73c", :name=>"Ry", :comment_count=>2}, {:id=>"5b45b99a-1a8a-4674-9ad5-f5c706d27315", :name=>"Hl", :comment_count=>2}], {:id=>"5b45b99a-1a8a-4674-9ad5-f5c706d27315", :name=>"Hl", :comment_count=>2}],
[[{:id=>"3e97741d-107d-4cab-ae11-323b1ef42668", :name=>"Y", :comment_count=>2}], {:id=>"3e97741d-107d-4cab-ae11-323b1ef42668", :name=>"Y", :comment_count=>2}],
[[{:id=>"63e812b2-e1b2-4528-acd2-f36d0f323dbd", :name=>"", :comment_count=>1}], {:id=>"63e812b2-e1b2-4528-acd2-f36d0f323dbd", :name=>"", :comment_count=>1}]]accounts_and_selection will create an array of accounts and then randomly select one of those accounts returning a two-tuple of that representation. This type of pattern is very helpful when you want to setup state in a system and then interact with one or more of the objects. Why create a generator that does the selection for you and not just select an element after the values from the generator that have been produced? Reproducibility. Being able to rerun the same generator with the same seed, producing the same array of accounts and selection is important when using this library in a testing context or in any context really.
Seeding the generator is simply providing a starting state to the pseudo random number generator so that you can repeatably produce the same values from your generator.
seed = 5647326586234654723645
accounts_and_selection.gen(5, seed) => [[{:id=>"0cc609b8-7ada-4192-be41-1f2b29369b14", :name=>"Kiq", :comment_count=>5}, {:id=>"cc8e373b-1a06-4d2c-adad-bb18f9d9b10f", :name=>"vU", :comment_count=>3}], {:id=>"0cc609b8-7ada-4192-be41-1f2b29369b14", :name=>"Kiq", :comment_count=>5}]
accounts_and_selection.gen(5, seed) => [[{:id=>"0cc609b8-7ada-4192-be41-1f2b29369b14", :name=>"Kiq", :comment_count=>5}, {:id=>"cc8e373b-1a06-4d2c-adad-bb18f9d9b10f", :name=>"vU", :comment_count=>3}], {:id=>"0cc609b8-7ada-4192-be41-1f2b29369b14", :name=>"Kiq", :comment_count=>5}]Taking our account_and_selection generator we created above, providing a seed value and a size we were able to reproduce the same generated value. In this example using the gen method, we can pass in the size and seed to the generator. Note the size value should also be seen as a state value as it will influence the value produced. The sample method does NOT provide this level of interactively and is intended for exploratory interaction in a console.
accounts_and_selection.to_enum(seed: 5647326586234654723645).take(6).to_a => [[[{:id=>"609b87ad-a192-4be4-91f2-b29369b14eee", :name=>"Kiq", :comment_count=>4},
{:id=>"cc8e373b-1a06-4d2c-adad-bb18f9d9b10f", :name=>"vU", :comment_count=>4},
{:id=>"004839da-0ea1-4545-bf74-211b1515e3c1", :name=>"OI", :comment_count=>2},
{:id=>"d59b453c-da7e-45d6-8d44-5a39123c1ce6", :name=>"", :comment_count=>1}],
{:id=>"d59b453c-da7e-45d6-8d44-5a39123c1ce6", :name=>"", :comment_count=>1}],
[[{:id=>"1f11bc82-c45a-4ab6-8528-eaf0a0b565f3", :name=>"sC", :comment_count=>4}, {:id=>"b2fa4c7b-0bb6-4ff0-9f69-ac709f703b02", :name=>"x", :comment_count=>3}], {:id=>"1f11bc82-c45a-4ab6-8528-eaf0a0b565f3", :name=>"sC", :comment_count=>4}],
[[{:id=>"f68e86b3-6b64-4e0d-a184-be9a04dd1101", :name=>"q", :comment_count=>1}, {:id=>"371fe60e-7cd8-40e0-bf8d-d06762978271", :name=>"", :comment_count=>1}], {:id=>"371fe60e-7cd8-40e0-bf8d-d06762978271", :name=>"", :comment_count=>1}],
[[{:id=>"30fa9545-b504-443d-8bb5-0a0a87acd2a3", :name=>"TZ", :comment_count=>4}, {:id=>"f8778a9b-2030-45ee-a82f-a7eddbad06c9", :name=>"b", :comment_count=>2}], {:id=>"f8778a9b-2030-45ee-a82f-a7eddbad06c9", :name=>"b", :comment_count=>2}],
[[{:id=>"d29f472d-55ea-4464-8b93-91d741b69db5", :name=>"oQc", :comment_count=>2}, {:id=>"448ae1c8-c6c6-40d5-a8ed-cd96dcf05c83", :name=>"", :comment_count=>1}], {:id=>"448ae1c8-c6c6-40d5-a8ed-cd96dcf05c83", :name=>"", :comment_count=>1}]]You can also leverage the to_enum method which will return an enumerable representing a lazy infinite sequence of values taken from the generator. Seen above with a provided seed, to_enum also has the ability to control the sequence of sizes passed to the generator when called. See the API docs for more details.
Sizing provides the ability for a generator to produce values of varying degrees of well, size. Size has different meaning depending on the context of the generator being used. In some cases generators don't honor the size parameter at all. Examples being uuid and identity. When a generator is called to produce a value the size that is passed in represents the upper bound of all possible sizes starting from zero that could be generated. Why? This is so that methods like to_enum, sample and gen don't return a predictable linear growth of values as they are passed ever increasing size values. It is also why this library and libraries like it seem to have the ability to walk thru progressively more complex values "randomly", perhaps challenging boundary assumptions of a model.
5.times { p gen.fixnum.gen(2) } => 2 0 -1 1 -2
5.times { p gen.string.gen(300) } => "oysO930W8B4QU02J05qEWPn6R6H7xTZKFGbG6Hpo28b"
"R021N1tw0927BXZP7GbzgRE4rA5t46785g27jsztMRlaz571XzHoi6yBv22ec97194yByN3KIYM3EX1XiRrA08Y32S5i2AvkevMRLClA8Xsb0N7R"
"3zFiC25U5495KY52FcB1B12txoA6xlFc83TJ97j9ytKSfi0rs1EwSILytRyMy2S5F70TrT6H457teJkVk5fr"
"VaLYheY512yh2qsj2MV31dl7oV56kWmmLlPSDIJwd74mOcyYfQft8N9756VfM5ExtBHql9TnRWl15j3beLww4p176G48s5q8bEWS0Nwcx7RX0WBz2nO412k7fWGi3nmxn8i156C45AHS27ttTB34sT0MiY63HWG0rbXLXnt41d3m5HiWmbnyh9yL36KH3TL4NMwK4vV0A9gZ6DpLrvPUWYaNYgEqQ0MGll1d2009l388upimkl71xaglmK97r7EDA491"
"82Q78FWpl1992nXtPUP2cF0rnM7jUPo0M6F8VgvbrQjHY4Var31H0aY94OF0Np6mlxM648S38LBvTrjZObsGn2eB9RPqzqWOlzMD0j71UKRZU90L7B95B5MdZMviaj5vml3JkkIODi6QZQWUobQt4Gr6b0mqR69UQ77897BuK2VjmFdNPLx4z8we7Bk0vU5o6DcJQgxOu7ZP"There a few methods that can be used to have finer grained control over the size being passed to a generator. See resize, scale, and for some generators not_empty in the API docs.
After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt with pry that will allow you to experiment.
To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.
Bug reports and pull requests are welcome on GitHub at https://github.com/smidas/radagen.
Radagen was greatly influenced by the generator API found in test.check and shares many of the same naming conventions. I have a great deal of gratitude to the contributors and maintainers of that library.
- More example documentation
- Make the
setgenerator honor min elements - Implement a Bignum generator with a good enough sampling distribution.
- Explore and implement the need for a splittable PRNG.
Radagen is released under the MIT License.
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