2026-02-12 –, RoboCon
Accelerate performance testing with Locust Script Generation and Execution via Robot Framework — an automated, keyword-driven approach to create, parameterize, and run scalable load tests. Seamlessly handle dynamic correlation, and generate detailed performance reports — empowering QA teams to validate APIs and user journeys with zero manual scripting and maximum reusability.
This solution empowers QA teams to streamline performance testing by integrating Locust with Robot Framework. Through a keyword-driven design, testers can define performance scenarios in simple, readable formats while the tool automatically generates Locust scripts, manages parameterization, and executes scalable load tests.
It supports dynamic correlation where the script writer specifies which field values (e.g., tokens, IDs) to extract and from which task they should be reused. Once defined, these correlations are dynamically handled at runtime, ensuring consistent and accurate test flows without hardcoded data.
By reducing manual effort in scripting while maintaining full control over test logic, this framework enhances reusability, maintainability, and CI/CD readiness. Built-in performance reports provide detailed insights into response times, concurrency, and bottlenecks — enabling QA teams to validate APIs and user journeys with efficiency and precision.
Learn to generate Locust scripts via Robot Framework with dynamic correlation
Categorize / Tags:Performance Testing,Locust,Automation
Is this suitable for ..?: Beginner RF User, Intermediate RF User, Advanced RF User Describe your intended audience:Automation and Performance Testing
In-Person or Online talk/workshop?:In-Person
A Software Engineer specializing in software development and cloud technologies, with a degree in Software Engineering and certifications in AI, Cloud, and QA. I have hands-on experience as an Integration Engineer, and QA Automation Engineering. I consistently adopts cutting-edge practices, and committed to innovation, and thrives on solving complex technical challenges in fast-paced environments.
Software Engineer focused on automation and quality engineering, with experience building test frameworks, improving system reliability, and supporting teams through AI and backend integrations. Worked across various environments on automated testing, framework enhancement, LLM development, and data processing pipelines. Comfortable using modern tools and cloud services to streamline workflows and reduce manual effort. Continuously learning and applying AI, LLMs, computer vision, and modern engineering practices to deliver stable and efficient software systems.