continuous integration

The holy grail of a modern QA professional is to achieve high quality while continuously delivering features to customers, and the only way to achieve that mission is via continuous testing and delivery. The most critical step in agile transformation and continuous delivery adoption is identifying the bottlenecks in the product development cycle. Tanya Kravtsov will describe tools and methods that facilitate that discovery process and explore ways you can use mind maps, innovation games, and more to identify, prioritize, and resolve bottlenecks. You'll learn ways to deal with the most common bottlenecks that cripple development progress: data, environments, and testing. Tanya will share her experiences with combating these bottlenecks and talk about tools and processes that helped her to overcome them.

Part of the path to DevOps requires adoption of agile methodologies. What does it mean for testing when you switch from the traditional waterfall model, with a few long release cycles per year, to the agile model, with changes occurring every two weeks? Here are five key factors to achieve the agile software testing necessary in DevOps.

DevOps is more than adopting the right set of tools; it's a cultural shift that incorporates testing at each stage of the agile project lifecycle. Continuous testing is key to unlocking this culture change because it weaves testing activities into every part of the software design, development, and deployment processes, which helps everyone involved communicate more, collaborate better, and innovate faster.

Artificial Intelligence (AI) is making it possible for computers to diagnose some medical diseases more accurately than doctors. Such systems analyze millions of patient records, recognize underlying data patterns, and generalize them for diagnosing previously unseen patients. A key challenge is determining whether a patient's symptoms and history are attributed to a known disease or other factors. Software testers face a similar problem when triaging automation failures. They investigate questions like, Is the failure due to a defect, environmental issue, or nondeterministic test script? Is there current or historical evidence to support one belief over another? Join Tariq King as he describes how test failures and flakiness can be modeled for machine learning (ML) as causal disease-symptom relations.