STAREAST 2019 - AI/ML
Thursday, May 2
The Era of Intelligent Testing
Existing QA solutions were built for a world where software changed infrequently. Highly adopted tools such as Selenium, Appium, and JUnit require a specialized skill set and too much maintenance, once you start factoring in the brittle nature of tests and the infrastructure required to run tests at scale. But there is still hope for QA in machine intelligence. Next-generation AI tools are here to help QA keep up with the agility of modern software delivery practices in two ways: by enabling manual testers who don't know how to code to automate, and by easily automating repetitive tasks so...
AI in Testing: A Moderated Panel Discussion
Artificial intelligence is the newest trend in software testing. But what is it, and how will it impact the tester's role, both today and in the future? What do you need to do to embrace this emerging technology? Adam Auerbach and Jennifer Bonine will moderate this panel discussion—which will include Jason Arbon, Dan Belcher, Tariq King, Jeff Nyman, and Jeremias Rößler—to give you an opportunity to hear the opinions of industry leaders about AI in testing. You will have a chance to drive the debate, so come prepared with all your AI questions.
Testing Large Data Sets with Supervised Machine Learning
Price rate is used to calculate an insurance premium based on the different insurance coverage. Every year the price rate is based on updated regulations, so after each change, the new price rate has to be tested for a large amount of data to make sure that the premium is correct based on the coverage. Testing fifty thousand data entries and their variations is impossible for any testing team. Alireza Razavi will present an AI automation testing framework designed to solve this testing problem. Discover how to use a supervised machine learning algorithm to determine the type of training...