Accelerating Performance Test Design with GitHub Copilot
In today’s software development environment, performance test design often faces challenges related to the time and effort required to create, optimize, and manage testing scripts. Manually writing and refining scripts for tools like LoadRunner and K6 can be both time-consuming and prone to errors. This presentation introduces GitHub Copilot as a solution, an AI-powered coding assistant that automates script generation and optimization. With the ability to generate API scripts at the click of a button, Copilot significantly reduces scripting time while enhancing accuracy and efficiency. Attendees will see how Copilot's capabilities include real-time code suggestions, automated code review, custom function generation, error handling, and optimization of existing scripts. These enhancements streamline the performance testing process, offering tangible benefits such as faster script creation, improved consistency, and enhanced overall productivity.
Kavin Arvind Ragavan is a Senior Performance Architect at Cognizant, specializing in performance engineering, chaos engineering, observability, and generative AI for performance testing. With 15 years of experience in performance engineering and over 3 years in chaos engineering and site reliability engineering, Kavin excels in designing and implementing cloud performance assurance and migration strategies. As a technology architect, he leverages AWS Cloud Platform and open-source frameworks to enhance application performance and stability. Kavin has created AI solutions for performance testing and authored blogs on chaos engineering in AWS. He has presented and published whitepapers at conferences such as ATA Global Testing Retreat, Chaos Carnival, and AWS Tech Summits, showcasing his expertise and contributions to the field.