Gain insights into property-based testing with PropEr in Erlang. Delve into foundational principles, common frameworks, custom data generators, and applying testing in realistic projects for robust code validation.

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Property-based testing relies on specifying some property of code, rather than unit tests which specify the expected output in response to some inputs. In this course, you’ll cover the concepts you need to get started, up to the point where you feel confident enough to use the most advanced features of PropEr with Erlang.

You’ll start smoothly with the basic and foundational principles of property-based testing. From there, you will cover some of the common frameworks, how to think in properties, how to write your own custom data generators, and more. You'll also see how you can use Property-Based Testing in a realistic project.

By the time you finish this course, you will be comfortable testing real-world applications using properties.

Property-Based Testing with PropEr in Erlang

# Generators

Generators are a huge part of where a property-based testing framework’s magic comes from. While developers 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 a useful framework, and great generators will directly impact how much trust you can put into our tests.

Generators are functions that contain a bunch of internal parameters that direct their randomness, how the data they create gets more or less complex, and information 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 since it lets us create all the kinds of Erlang data we might need. You’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. They also contain some special data types that allow us to do things like making sure integers are in a given range or making one generator that represents 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

Targeted Properties

Stateful Properties

Case Study: Bookstore

State Machine Properties

Conclusion

Default Generators

Generators