OVERVIEW
The Certified GenAI-Assisted Test Engineer (GenAiA-TE) program is a four-day certification course designed to equip software testing professionals with practical knowledge and hands-on experience in applying Generative AI (GenAI) to modern testing practices.
This program introduces participants to GenAI-assisted testing across the software development lifecycle, including prompt engineering, requirements review, test design, test data generation, bug advocacy, and AI adoption strategies in testing. Through structured learning and practical application, participants will learn how to leverage Large Language Models (LLMs) to enhance testing effectiveness, efficiency, and quality while preparing for AI-driven testing environments.
By the end of this program, participants will be able to:
- Apply GenAI techniques in software testing activities
- Use effective prompt engineering techniques for testing tasks
- Conduct GenAI-assisted requirements review
- Design and optimize test cases using AI-supported approaches
- Generate test data using GenAI techniques
- Improve bug advocacy and reporting using NLP-driven methods
- Understand AI adoption strategies and future directions in testing
4 Days (9.00am – 5.00pm)
The program duration and delivery format can be customized based on organizational requirements.
- Test Engineers
- QA Engineers
- Software Testers
- Test Leads
- Professionals involved in software testing and quality assurance
PROPOSED OUTLINE/AGENDA
DAY 1 (9am to 5pm)
- Icebreaker
- Introduction, keynotes, and program objectives
- Introduction to Artificial Intelligence and Large Language Models (LLMs)
- Evolution of software testing
- Benefits and challenges of GenAI in testing
- Fundamentals of prompting
- Common prompting patterns
- Using Markdown for clarity and structure
DAY 2 (9am to 5pm)
- Recap of Day 1 learning
- Adapting prompts to changing contexts
- Mental models for effective prompting
- LLM evaluation and calibration
- Reviewing prose and data requirements using GenAI
- Formats and techniques for AI-assisted requirements validation
DAY 3: Test Design & Data Generation
(9am to 5pm)
- Recap of Day 2 learning
- Systematic test generation techniques
- Exploratory and non-functional testing
- Diagram-based and linguistics-based testing
- Data representation techniques
- Fuzzing and regular expression (Regex) approaches
DAY 4: Bug Advocacy & AI Roadmap
(9am to 5pm)
- Recap of Day 3 learning
- Writing clear and effective bug reports
- Applying NLP techniques to improve bug reporting
- Custom GPTs and AI configurations
- Developing an AI adoption strategy for testing teams
- Group presentations
- Feedback and certification guidance
PROGRAM METHODOLOGY
- Instructor-Led Sessions – Structured explanation of GenAI and testing concepts
- Hands-On Practical Exercises – Applying GenAI tools in real testing scenarios
- Group Discussions – Knowledge sharing and collaborative learning
- Case-Based Learning – Real-world testing use cases
- Reflection & Feedback Sessions – Reinforcing learning and readiness for certification
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