Borrowing from recent real-world experiences, Casey will discuss how a sub-par test suite began to cause delays and negatively affect a large production project with a national audience. He'll share what his team learned after deciding to dive head-first into faster and more effective testing, including:

• The key differences between unit tests and integration tests, and how to distribute them appropriately
• Dropping the Django framework's fixture system in favor of read-only test data to create a test suite that adapts alongside a complex and changing data model
• Using the python Mock library to:
  • Mock object instances
  • Mock python modules for very precise feature testing
  • Mock very complex testing situations (such as overridden class methods, etc)

We'll look at real code samples and the tests used to vet them, demonstrating the ways Casey's team was able to update their tests to use these standards and quantify the benefits. And finally, we'll discuss how the quest for faster tests lead to a full blown testing philosophy and better code.