Tests are great.

Except when they’re not.

Almost every developer who’s worked with tests has encountered a test suite that caused a lot of pain.

Some of them just don’t protect us when we need them, some are flaky, some keep breaking because of unrelated changes, some take hours to debug whenever they fail.

And while every company is different, there are definitely patterns. A lot of these problems are the result of some common pitfalls that trap many teams. These pitfalls might be common, but they're not easy to spot - I’ve seen all of them happen in strong, capable, experienced teams. Most of these I fell into myself at least once.

In this session, we'll take a look at a selection of problematic testing choices, with examples that show these in the context of common Python frameworks and libraries. We'll discuss how to identify them, what problems they might cause and what alternatives we have so we can save ourselves the pain.