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

S2E4 - Júlio De Lima - Machine learning to understand performance testing results

Making sense of software quality... that’s what testers aim to do and that’s what this podcast is all about. Join Abstracta COO, Federico Toledo, PhD as he dares to interview some of his role models and industry leaders who are awesome at what they do to get advice that can help us be better testers and enable our teams to create higher quality software. 

Expect insights on today’s most pressing topics and good practices from the creators and collaborators of your favorite software testing tools, methodologies, conferences, etc. An Uruguayan in Silicon Valley, you’ll also hear Federico share his views from over 15 years of training testers and working with clients like CA Technologies and Shutterfly.

S2E4 - Júlio De Lima - Machine learning to understand performance testing results

Quality Sense, a Software Testing Podcast