Connor Dodge believes that data is the most valuable commodity in the world, and that testers generate some of the most valuable data in product development organizations. Test data can inform release schedules, aid in decision-making, and shape the direction of the product. 

Max Saperstone tells the story of how a health care company striving to get to continuous releases built up their automation to secure confidence in regular releases. Initially, as no test automation existed, Max was able to take an opportunity for greenfield test automation and, in the span of twelve months, develop over two thousand test cases. A pipeline was created to verify the integrity of the automated tests and build Docker containers for simplified test execution. These containers could be easily reused by developers and the DevOps team to verify the application. Join Max as he walks through the feedback loop that was created to allow application verification to go from hours to minutes. Max will share his choices of BDD tooling, integrated with WebDriver solutions, to verify the state of web and mobile applications.

We are often reminded by those experienced in writing test automation that code is code. The sentiment being conveyed is that test code should be written with the same care and rigor that production code is written with. However, many people who write test code may not have experience writing production code, so it’s not exactly clear what is meant. And even those who write production code find that there are unique design patterns and code smells that are specific to test code. Join Angie Jones as she presents a smelly test automation code base littered with several bad coding practices and walks through every one of the smells. She'll discuss why each is considered a violation and via live coding, she will demonstrate a cleaner approach. While all coding examples will be done in Java, the principles are relevant for all test automation frameworks.

For many test organizations, the first hurdle to automating the testing of a product is deployment of that product in its test environments. Infrastructure as code can be used to facilitate the basic processes of provisioning servers, from bare metal to virtual to cloud, as well as configuration management of the software that resides on the servers. Off-the-shelf infrastructure-as-code tools such as AWS CloudFormation, Chef, Puppet, and Ansible provide less expensive alternatives to developing proprietary in-house deployment solutions. Join Kat Rocha to learn how infrastructure as code can better align test and production environments and reduce problems that arise from configuration drift. We will explore how to use some Infrastructure-as-code tools to facilitate automation and improve testing.