In this Quality Sense episode, I had a chat with Júlio de Lima, an engineer at Capco, who recently completed his master’s degree in Electrical Engineering and Computing (Artificial Intelligence) and also co-founded GaroaQA, a meetup group with four locations across Brazil and over 2,000 members.

Episode Highlights

• The complexity of analyzing the huge amounts of data that software performance tests provide
• Using machine learning to solve data issues by giving meaningful insights about what happened during test execution
• How he used 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

For related links and the transcript, check out this article: abstracta.us/podcast/julio-de-lima-machine-learning-performance-testing