Course Detail

AI-Assisted Test Automation

Overview
Course Details
Course Fee

Course Overview

Gain knowledge and practical skills on how AI may speed up the execution of tasks that a test automation engineer faces daily and improve their average performance.

Mode of Delivery

Self-paced course (Online Learning)

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)

Registration Fee

A Registration Fee of $10.90 (Incl. of 9% GST) will be collected upon course registration, and It is non-refundable.
* In the event of any class cancellation by Eduquest, the Registration fee will be refunded

Miscellaneous Fees (Incl. of 9% GST)

Admin Fee for Absent and Reschedule: $32.70

Admin Fee for Make-up Session for Partial Absent, Appeal & Re-assessment: $54.50

Miscellaneous Fees refer to any non-compulsory fees which the trainees pay only when applicable. Such fees are normally collected by Eduquest when the need arises.

Fees indicated are per pax and not per proforma invoice or invoice.

Schedule

More Schedule

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This Course Includes:

Duration: (14 Hrs)

Language: English

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