Taking Your Automation Framework to the Next Level Using Machine Learning

James Farrier

Summary:

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. You will learn a robust variety of actionable machine learning techniques that can be applied to automated testing to dramatically increase the value you get from your automated testing.

Presenter Bio:

James Farrier has been working in testing since 2004. He always enjoyed finding the complex defects rather than writing code, so moving into automated testing was a natural career path. Previously he's been a test lead and manager, always with a focus on the technical side of testing, but in the last year he's been working on his own startup in the testing space, utilizing AI to predict which tests need to be executed. James has spoken previously at Selenium Conference 2015 on visibility in testing and has recently talked at Meetups on AI in testing in Auckland, London, Washington DC, and New York.