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

## Summary
In this chapter, we’ve walked through a realistic integration test suite written as stateful properties. All we had was a model layer for a bookstore using a PostgreSQL instance without any tests. We first started by drawing up the generators for our base data types, which helps do some broader testing to make sure the system works without throwing exceptions. That’s where we encountered and resolved our first encoding bug.

With this out of the way, we established the initial model, using a wrapper module (a shim) to allow for as much determinism as possible. As we've seen, this approach tends to create a bit more repetition with precondition-heavy code, but makes tests more decideable and simpler to debug. We’ve also seen this at work as we refined the model through multiple failures and learned about more tricky aspects of using SQL.

Additionally, we’ve been through a round of debugging and we’ve seen how PropEr shrinks stateful property command sequences to debug our models before doing a final check with parallel tests.

## What's next
We’ll learn how to create and operate a  specialization for systems that represent finite state machines.

# Summary
In this chapter, we’ve walked through a realistic integration test suite written as stateful properties. All we had was a model layer for a bookstore using a PostgreSQL instance without any tests. We first started by drawing up the generators for our base data types, which helps do some broader testing to make sure the system works without throwing exceptions. That’s where we encountered and resolved our first encoding bug.

With this out of the way, we established the initial model, using a wrapper module (a shim) to allow for as much determinism as possible. As we've seen, this approach tends to create a bit more repetition with precondition-heavy code, but makes tests more decideable and simpler to debug. We’ve also seen this at work as we refined the model through multiple failures and learned about more tricky aspects of using SQL.

Additionally, we’ve been through a round of debugging and we’ve seen how PropEr shrinks stateful property command sequences to debug our models before doing a final check with parallel tests.

# What's next
We’ll learn how to create and operate a  specialization for systems that represent finite state machines.

Here's a summary of what we learned in this chapter.

Wrapping Up

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

Wrapping Up

Summary