Ebin I
The 9th edition of the Global Testing Retreat 2024!
About Speaker
Ebin I, as a Programmer Analyst with a specialization in performance testing and Engineering. My expertise lies in using industry-leading tools like LoadRunner, AppDynamics, and Splunk, which allows to ensure that systems perform optimally and handle the demands placed on them. My focus is on analyzing and identifying performance bottlenecks, delivering high-quality, efficient solutions for both web and application environments. Beyond my technical expertise in performance monitoring, I’m also deeply passionate about the rapidly evolving field of Generative AI. This keen interest, coupled with my strong foundation in Python models, drives me to explore how AI can enhance performance testing and also eager to integrate AI-driven innovations into my work, merging traditional performance engineering techniques with the latest advancements in artificial intelligence for more comprehensive and forward-thinking solutions.
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As software development techniques evolve on a daily scale, ensuring the robustness and efficiency of applications is paramount. This workshop demo focuses on the innovative approach of implementing the Generative Al in software testing, particularly emphasizing AI- driven exploratory performance testing. Generative Al shortly the ‘GenAl’ has its own ability to simulate human-like interactions and generate diverse test scenarios which brings a new dimension to exploratory testing, holding the upper hand than the traditional testing methods which fall behind in identifying subtle bugs and bottlenecks. Automating the performance testing with the help of web-automation tools and GenAl which navigates through the application and perform various actions and uncover, hidden issues. The Al’s ability to generate realistic and varied workloads ensures that the application can handle different user behaviors and peak loads efficiently. In this demo, we can witness the seamless integration of Al driven exploratory testing with performance testing on a web-application. This demo showcases the setting up of the environment, configuring the Al agent, and running the tests while Al agent will autonomously interact with application and simulate real-world user interaction, finally producing the logs of its activities. This log will be later analyzed to identify the performance bottlenecks and potential areas for optimization. Integration of GenAI in performance testing with the help of class leading technologies and tools will reinforce applications from bottlenecks and performance hiccups.
Workshop Table of Content with suggested duration:
- Introduction (3 minutes)
- Hardware and Software Requirements (3 minutes)
- Pre-requisites (3 minutes)
- Setting up the Environment (4 minutes)
- Setting up the Web Application (4 minutes)
- Configuring the AI Agent (5 minutes)
- Running the Web Application (5 minutes)
- Executing the Al-Driven Exploratory Testing Script (5 minutes)
- Performance Testing Integration (5 minutes)
- Analyzing Results (5 minutes)
- Conclusion (3 minutes)
Lab Requirements: Hardware requirements:
Computer: A computer with at least 8GB RAM and a multi-core processor (e.g., Intel i5 or higher) Storage: Minimum 10GB free disk space
- Internet Connection: Reliable internet connection for downloading dependencies and accessing the OpenAI API
Software Requirements:
- Operating System: Windows, macOS, or Linux
- Python: Python 3.8 or higher
- Web Browser: Google Chrome or Mozilla Firefox
- Browser Driver: Chrome Driver for Chrome or Gecko Driver for Firefox
- OpenAI API Access: An OpenAl account with API access
- Selenium: Selenium WebDriver library
- Load Testing Tool: JMeter or similar performance testing tool.
Pre-Requisites :
- Access for OpenAI API key
