Reducing the Scope of Load Test Analysis
Load testing execution produces a huge amount of data. Investigation and analysis are time-consuming, and numbers tend to hide important information about issues and trends. using machine learning is a good way to solve data issues by giving meaningful insights about what happened during test execution. Julio Cesar de Lima Costa will show you how to use K-means clustering, a machine learning algorithm, to reduce almost 300,000 records to fewer than 1,000 and still get good insights into load testing results. He will explain K-means clustering, detail what use cases and applications this method can be used in, and give the steps to help you reproduce a K-means clustering experiment in your own projects. You'll learn how to use this machine learning algorithm to reduce the scope of your load testing and getting meaningful analysis from your data faster.
Júlio de Lima is a principal QA engineer with ten years of experience in software testing. He's currently pursuing a master's degree in electrical and computational engineering with a focus on AI at Mackenzie University. Júlio has worked in all levels of testing: unit, system, and API integration (REST and SOAP), UI (desktop, web, and mobile), performance, and acceptance (UAT). He's implemented functional software testing automation process in many companies using tools like Selenium WebDriver and TestComplete, as well as nonfunctional automation testing using JMeter. Júlio has the following software testing certifications: CTFL, CTFL Agile Tester, CTAL Test Manager, CTAL Test Automation Engineer, CBTS (Brazilian certification), and SoapUI Pro. Sporadically, Júlio works as a guest post-graduate professor at the universities Uniasselvi (Santa Catarina) and Unicesumar (Paraná).