Comparative Analysis of AI-Supported and Manual JMeter Tests: The Role of Generative AI and LLM in Software Performance Testing

Abstract

This paper addresses the challenges of conducting software performance testing and the challenges encountered in the pre-testing process. The focus is on the importance of software performance testing and evaluation methodologies. At the same time, the main theme of large language models (LLM) and the characteristics of modeling and its role in this process are examined. The overall aim of the study is to investigate how Generative AI- Large Language Models (LLM) can be used efficiently in performance testing in important stages such as creating test plans, constructing test profiles, creating and preparing data, and interpreting the reports received as a result of the tests. The advantages of Artificial Intelligence, more precisely Generative AI- Large Language Models (LLM), are discussed in terms of optimizing the processes carried out in performance testing in a positive sense and accelerating the process. This study is envisioned as a contribution to the traditional methods used in performance testing. The potential of Generative AI-Large Language Models (LLM) to effectively solve the problems in traditional testing methods and to create more efficient testing processes may guide the development of performance testing methodologies in the future.