Course Description
In the rapidly developing realm of test automation, integrating Large Language Models (LLMs) and related generative AI tools such as ChatGPT and GitHub Copilot is becoming increasingly essential for test automation engineers aiming to lead the industry.
The AI-Assisted Test Automation course is carefully tailored for automation professionals eager to elevate their expertise.
Throughout this course, you will explore the extensive capabilities of LLMs and AI tools that are crucial for enhancing the efficiency of automated testing routines.
You'll delve into the practical applications of these tools in automating test scripts, preparing documentation, setting up the environment, and streamlining test processes. Furthermore, a major emphasis will be placed on mastering the techniques of interacting with LLMs effectively to maximize their utility in test automation scenarios.
Specifically designed for the needs of test automation engineers, this course will empower you with the knowledge and skills needed to flawlessly incorporate LLMs into your existing test automation frameworks. By the conclusion of this course, you will be proficient in utilizing generative AI tools, ready to revolutionize your test automation strategy and significantly boost your project outcomes.
Module 1: Mastering Large Language Models
- Introduction to Large Language Models (LLMs)
- LLMs and Conversational Tools
- The Art of Prompting
- Using the CREATE Framework
- Getting Started With ChatGPT
- Inline Tools
- Inline Tools: The Art of Context
- Conversational Tools vs. Inline Tools
Module 2: Deciding the Scope of Test Automation
- Introduction to the Automation Testing Life Cycle (ATLC)
- Application Requirements and Automation Abilities
- Setting Up Automation Priorities
- Documentation, Cost, and Team Size
- Practical Tasks
Module 3: Choosing the Right Automation Tool
- List of Tools and Their Evaluation
- Additional Tasks Within Choosing a Tool
- Practical Task
Module 4: Plan, Design, and Strategy
- Automation Test Strategy
- TAF and Reusable Test Scripts, Libraries, and Functions
- Requirements and Timelines
- Practical Tasks
Module 5: Setting Up the Test Environment
- Requirements and Automation Tool Installation
- Version Control and Test Data
- Practical Task
Module 6: Test Script and Execution
- Using GitHub Copilot for Test Script and Execution
- Test Script Development and Conversational Tools
- Test Script Maintenance With GitHub Copilot
- Practical Task
Module 7: Test Analysis and Reporting
- Test Failures and Reports
- Practical Task
Module 8: Extras
- Using OpenAI API With Postman
- EliteA: a Tool for LLM Asset Management and Collaboration
Learning Outcomes
The primary goal of this course is to provide knowledge and practical skills on how AI may speed up the execution of tasks that a midlevel test automation engineer faces daily and improve their average performance.
After you complete the course, you should be able to:
- Identify and effectively address challenges when working with LLMs, ensuring seamless integration into existing workflows and processes
- Use LLM-based conversational AI tools to enhance everyday tasks in the test automation process, identifying specific tasks that can be accelerated to optimize workflows and reduce time spent on repetitive elements
- Comprehend the limitations and capabilities of conversational AI tools in addressing various test automation tasks, from deciding the automation scope to result documentation
- Create and refine complex prompts, drawing on real-life examples, to generate effective solutions using generative AI tools, enhancing their potential to support test automation tasks
Who Should Attend
Test automation engineers aiming to integrate LLMs and AI tools into their frameworks.
Professionals looking to improve the efficiency of test automation while navigating AI-driven challenges.
Duration
(14 Hrs)