Gain insights into property-based testing with PropEr in Elixir. Learn foundational principles, custom data generators, and advanced concepts for effective real-world application testing.

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Property-based testing relies on specifying some property of code, unlike unit tests, which specify the expected output in response to some inputs. In this course, we'll cover the concepts we need to feel confident about using even the most advanced features of PropEr with Elixir.

We'll start with the basic and foundational principles of property-based testing, see what the framework offers us to get started, make our way through thinking in properties, write our own custom data generators, and then learn more advanced concepts. We'll also see how you can use property-based testing in a realistic project.

By the time we finish this course, we should feel comfortable testing real-world applications using properties.

Property-Based Testing with PropEr in Elixir

# Generators

Generators are a huge part of where a property-based testing framework’s magic comes from. While we do the hard work of coming up with properties, the efficiency with which they will be exercised depends on what kind of inputs will be passed to them. A framework with inadequate generators is not an interesting framework, and great generators will directly impact how much trust we can put in our tests.

Generators are functions that contain a bunch of internal parameters that direct their randomness. The data they create gets more or less complex based on the information they contain about other generators that can be used as parameters. We don’t need to know how their internals work to use them. From our point of view, they’re just functions that can be combined. However, being familiar with what data they can generate is important as it lets us create all the kinds of data we might need. We’ll likely even want to use generators outside of a testing context because they are just that nifty.


# Default generators

PropEr comes with quite a few basic data generators. They tend to represent all the relevant basic data types of the language (aside from opaque data like PIDs, references, or sockets), with some special ones that allow us to ensure that integers are in a given range and make one generator represent multiple other generators.

|**Generator**|**Data Generated**|**Sample**|
| - | :- | :- |
|`any()`   |Any Erlang term PropEr can produce. Same as `term()`   | Any of the samples below  |
|`atom()`   |Erlang atoms   | 'ós43Úrcá\200'  |
|`binary()`   |Binary data aligned on bytes|<<2,203,162,42,84,141>>|
|`boolean()`   |Atoms `true` or `false`. Also `bool()` | `true`, `false`|
|`choose(Min, Max)`   |Any integer between `Min` and `Max` inclusively. Same as `Range(Min, Max)` |`choose(1, 1000)` => 596|
|`float()`  |Floating point number. Also `real()`  |4.982972307245969 |
|`integer()`|An integer |89234 |
| `list(Type)`|A list of terms of type `Type` |list(`boolean()`) => [`true`, `true`, `false`]|
| `non_empty(Gen)`|Constrains the binary generator into not being empty |`non_empty(list())` => [abc]|
|`number()` |A float or an integer  | 123 |
| `range(Min, Max)` |Any integer between `Min` and `Max` inclusively |`range(1, 1000)` => 596 |
|`string()` | Equivalent to `list(char())`, where `char()` returns character codepoints between 0 and 1114111 inclusively|"^DQ^W^R/D" (may generate strange escape sequences!) |
|`{T1, T2, ...}`|A literal tuple with types in it | `{boolean(), char()}` => `{true, 1}`|

Get introduced to generators and take a look at a few default generators provided by PropEr.

Default Generators

Foundations of Property-Based Testing

Writing Properties

Thinking in Properties

Custom Generators

Responsible Testing

Properties-Driven Development

Shrinking

Stateful Properties

Case Study: Bookstore

State Machine Properties

Conclusion

Default Generators

Generators