Sivaa Deivasigamani
The 9th edition of the Global Testing Retreat 2024!
About Speaker
Sivaa Deivasigamani
Senior Associate Projects
Cognizant Technology Solutions
With over 9 years of experience in Performance and Automation Testing, I have honed my skills in tools like HP Load Runner, IBM Rational Performance Tester, Neoload, Gen AI, and Selenium WebDriver. My expertise lies in understanding and analyzing test requirements, tracking changes, and maintaining test requirements, including functional, integration, and regression testing. I am well-versed in all phases of the Software Testing Life Cycle (STLC) and have extensive experience in writing test cases, preparing test plans, executing automation test scripts, reporting and tracking defects, and preparing test result reports. I have worked on projects using both Waterfall and Agile methodologies and have conducted performance test executions and analyzed the results. My performance testing experience includes using IBM Rational Performance Tester, JMeter, and HP Load Runner, performing load, stress, soak, and volume testing for banking and telecom applications. I possess domain expertise in banking and telecom and have strong written, communication, and interpersonal skills, making me a proven team player with an analytical mindset for problem-solving and delivering solutions.
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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