Test-driven development is supposed to help us refactor our code safely, but we often find that when we refactor our code, we also have to refactor our tests. What was supposed to add safety becomes a burden requiring time and effort. Writing good unit tests is a critical skill that developers need to master in order to get the most benefit from test-driven development. Tests must be unique, written at the right level of abstraction, and implementation-independent in order to be most valuable. In this session, David Bernstein will cover effective techniques for doing TDD that support building useful tests and quality code. You’ll learn how to approach TDD in a way that yields the right number and kind of tests to support improved refactoring. Working through a few code examples, you’ll see how many assertions are required to specify a linear range, exceptions, and other boundary conditions.

Agile teams ship frequently and change things all the time--by design. In the middle of all this, agile teams need to make sure that their core user flows like sign-in, shopping carts and search features still work on every new build. Traditional testing is a poor fit for agile teams. AI changes all that with the ability to create automated verification for new user flows, and automatically maintain those verification steps even when the application changes. AI changes the game so that agile teams can move faster, with less risk or worry about breaking the app. Jason Arbon walks you through real world running examples of AI-based UI flow verification, and shares which machine learning approaches work the best. If your agile team struggles with testing and worries about quality, see how AI is changing the game and freeing up teams to move faster, with higher quality.

In an agile world where having dedicated testers can be a controversial idea, manual testing is a tough sell. Not all contexts have their testing automated, so what is a manual tester to do when it comes time to release? Your team starts asking about regression testing, requests estimates, and expects justifications for the time being spent. Intuition isn't the answer, and retesting everything is not an option. In this session, seasoned tester Brendan Connolly will share his five core values for focusing your regression testing efforts: consistency over correctness, behaviors over bugs, intent over implementation, conformity over complexity, and common over complete. These values not only help with decision-making, but also serve as a heuristic foundation for understanding and communicating about regression testing, as well as define a clear intent and context for all your future regression tests.

Automation fails frequently in companies due to a variety of reasons, including poor team communication, lack of skills, flaky tests, and inadequate understanding of test coverage. Even when things are going well, the automated tests sometimes grow to a size where the test suites take too long to execute for the run to be viable. James Farrier is a test automation architect who will show you ways to leverage machine learning to address these challenges. You'll learn how to determine which tests are valuable to run after each commit or build in order to cut down the suite run time, how to automatically close and open defects based on test run results, and how to separate a test into different test runs to keep track of tests in different states. Finally, he will show you how to create a results dashboard that allows for team collaboration and a better understanding of test coverage so that testing can be further streamlined.