Pradipa Thennarasu
The 9th edition of the Global Testing Retreat 2024!
About Speaker
With over 16 years of experience in Performance Testing and Engineering, I possess exceptional technical and analytical skills. I am proficient in leading performance tools and trending technologies, and I am a fast learner who adapts well to diverse teams to meet business goals. I have led teams to certify the end-to-end performance of legacy and microservice architecture applications.
I have extensive exposure to projects driven by the latest technologies like Azure and AWS cloud, and I have strong expertise in Agile concepts, REST API frameworks, and service virtualization. I execute various types of load simulations, monitor and analyze performance metrics using tools like AppDynamics, Dynatrace and perform root-cause analysis by examining logs, heap dumps, and thread dumps to suggest performance improvements. I track user stories through JIRA and Confluence and have experience in NFR gathering, volumetric analysis, capacity planning, new proposals, and test estimation.I am skilled in leading testing and monitoring tools such as LoadRunner, Neoload, Katalon, JMeter, Dynatrace, and AppDynamics. Conducted disaster recovery/failover testing to ensure application performance remains unaffected. I analyze SQL queries and AWR reports to suggest performance improvements and have strong hands-on experience in creating test plans, test scripts, test execution, analysis, and reporting. I evaluate application performance by comparing baseline vs. benchmark and have managed migration activities from on-premise to cloud-hosted applications. I document performance risk assessments and lessons learned for future releases. I have conducted load, stress, scalability, and endurance tests for online banking applications under heavy load and proposed solutions for large insurance deals, working on test strategy documents and obtaining stakeholder approval. I have been involved in R&D on GenAI integration with Neoload, producing successful results.
More Speakers
This comprehensive study delves into the amalgamation of GenAI Copilot, a no-code Artificial Intelligence platform, with NeoLoad, a widely recognized load testing tool. The central theme of this research revolves around the utilization of GenAI Copilot to inject tailored code into the NeoLoad script, thereby enhancing its functionality and adaptability. NeoLoad is renowned for its as-code Domain-specific Language (DSL) that supports both YAML and JSON formats. This feature empowers users to articulate load testing scripts, variables, and other pivotal test execution settings in a structured and organized manner. The DSL provides a robust foundation for defining and managing complex load testing scenarios, thereby streamlining the testing process. The integration of GenAI Copilot with NeoLoad brings a new dimension to this process. GenAI Copilot, with its advanced AI capabilities, allows for the customization of these scripts without necessitating extensive coding expertise. This means that even individuals with limited coding knowledge can effectively tailor the scripts to meet their specific testing requirements. This democratization of script customization could potentially revolutionize
the way load testing is conducted. The paper provides an in-depth examination of this integration, shedding light on how it can augment the efficiency and effectiveness of load testing. It meticulously analyses the various facets of this integration, exploring how the synergy between GenAI Copilot and NeoLoad
can lead to more accurate, efficient, and comprehensive load testing. The findings of this research could have far-reaching implications for the realm of software testing and development. In today’s fast-paced development environments, where swift and efficient load testing is of paramount importance, the ability to customize testing scripts quickly and easily could be a game-changer. It could lead to more streamlined testing
processes, quicker identification of bottlenecks and issues, and ultimately, the development of more robust and reliable software applications. In conclusion, the integration of GenAI Copilot with NeoLoad represents a significant
advancement in the field of load testing. By enabling easy customization of testing scripts, it has the potential to make load testing more accessible, efficient, and effective. This could ultimately lead to improved software quality and performance, benefiting both developers and end-users alike. This research serves as a valuable resource for anyone interested in understanding the potential of AI in enhancing load testing procedures
