STAREAST 2018 - Test Analytics, AI/ ML
Wednesday, May 2
AI-Driven Testing as a Service: Fad, Fiction, or Future?
Advances in artificial intelligence (AI) and machine learning (ML) are leading to a new generation of software, which is becoming self-adaptive, autonomous, and smart. Academic researchers and industry practitioners are investigating how these new AI and ML technologies can be leveraged to improve software testing and testing services. A handful of testing-as-a-service (TaaS) vendors already offer services that use AI bots to perform some functional and performance testing. How well do they live up to their claims? Can they be used as an effective substitute or supplement for human testers...
Machine Learning Heralds the End of Selenium
PreviewSelenium has been the cure for free and low-cost browser testing for years, and—in the world of agile, mobile, DevOps, and browserless interfaces—it is showing its age. Comparing Selenium to what’s coming, Jason Arbon says that machine learning and data analytics will become the new standard for test automation. With Selenium, test engineers suffer from the pains of broken element identification; broken, buggy, and partially implemented mobiletest capabilities; exploding costs of building abstraction layers on their apps; brittle test code when the application under test changes;...
Machine Learning and Data Science for Quality and Performance Engineering
PreviewManaging the quality and performance of complex systems requires more than simply executing test cases and running load tests. You need to perform careful analysis of test results and production metrics. The sheer amount of data generated in production and testing makes analysis a huge challenge that is often left wanting. With the magic of machine learning (ML) and the application of data science techniques, you have the opportunity to derive valuable and actionable information from big data. Gopal Brugalette shares the basic concepts behind ML, covering clustering, classification...