Testing with Generative AI —ISTQB Software Testing Certification Training

Course Description

Why choose ALPI for ISTQB Testing with Generative AI (GenAI) certification training?

We use certified live instructors in both our in-person and virtual classes so you can ask questions and get answers right away.
We focus on real-world examples.
We teach using interactive, hands-on exercises.
This ALPI course is eligible for a free refresher guarantee so you can re-take the course within 4 months at no additional charge. Plus, if you meet the criteria, you could also re-take your exam for free. Contact us for information about this unique benefit that gives you peace of mind.

The ISTQB Testing with Generative AI Certificate Course is a three-day course explaining the fundamentals of Generative AI and its application to software testing. This course addresses the ISTQB Testing with Generative AI Syllabus.

The course includes exercises and practice exams to highlight key aspects of the syllabus and to help participants understand and practice the concepts and methods presented.

This course provides participants with the knowledge and skills necessary to become an effective member of testing team leveraging Generative AI. It explains the fundamental concepts of using Generative AI for testing on software projects including methods and practices around effective prompt engineering and managing risks associated with Generative AI. We suggest that attendees hold the ISTQB Foundation Level certificate, especially if they intend to take the ISTQB Testing with Generative AI exam, but non-certificate holders can also benefit from this course.

By the end of this course, an attendee should be able to:

Understand the fundamental concepts, capabilities, and limitations of Generative AI
Develop practical skills in prompting large language models for software testing
Gain insight into the risks and mitigations of using Generative AI for software testing
Gain insight into the applications of Generative AI solutions for software testing
Contribute effectively to the definition and implementation of a Generative AI strategy and roadmap for software testing within an organization

This course prepares you for the ISTQB Testing with Generative AI exam. You have the option to add the ISTQB exam for $199 when registering for class. Passing the exam will grant you an ISTQB CT-GenAI certification. Extended time requests should be made 2 weeks prior to class start for non-native English speakers.

For participants attending class remotely (Virtual Live), the exam can be scheduled online from home/office or by visiting a test center. Visit ISTQB Online Exam Information and Locate a Test Center for details.
For participants attending class in Chevy Chase, MD, the exam will be administered on last day of class, ending by 5pm, so please plan your travel accordingly.

Duration

3 day(s)

Time

9 - 3 ET

Price

$2,025

Labs

Exercises reinforcing Learning Objectives help to understand and apply topics in the course.

Intended Audience

The target audience for this course includes:

Software testers
Senior testers
Test analysts
Test leads
Managers including test managers, project managers, quality managers

Prerequisites

You must have obtained an ISTQB Foundation Level Certification (CTFL) to be eligible for the Testing with GenAI Certification.

Prior to attending class please download and review the following document: Testing with GenAI Syllabus

Outline

Introduction to Generative AI for Software Testing

Generative AI Foundations and Key Concepts
- AI Spectrum: Symbolic AI, Classical Machine Learning, Deep Learning, and Generative AI
- Basics of Generative AI and LLMs
- Foundation, Instruction-Tuned and Reasoning LLMs
- Multimodal LLMs and Vision-Language Models

Leveraging Generative AI in Software Testing: Core Principles
- Key LLM Capabilities for Test Tasks
- AI Chatbots and LLM-Powered Testing Applications for Software Testing

Prompt Engineering for Effective Software Testing

Effective Prompt Development
- Structure of Prompts for Generative AI in Software Testing
- Core Prompting Techniques for Software Testing
- System Prompt and User Prompt

Applying Prompt Engineering Techniques to Software Test Tasks
- Test Analysis with Generative AI
- Test Design and Test Implementation with Generative AI
- Automated Regression Testing with Generative AI
- Test Monitoring and Test Control with Generative AI
- Choosing Prompting Techniques for Software Testing

Evaluate Generative AI Results and Refine Prompts for Software Test tasks
- Metrics for Evaluating the Results of Generative AI on Test tasks
- Techniques for Evaluating and Iteratively Refining Prompts

Managing Risks of Generative AI in Software Testing

Hallucinations, Reasoning Errors and Biases
- Hallucinations, Reasoning Errors and Biases in Generative AI
- Identify Hallucinations, Reasoning Errors and Biases in LLM Output
- Mitigation techniques of GenAI hallucinations, reasoning errors and biases in software test tasks
- Mitigation of Non-Deterministic Behavior of LLMs

Data Privacy and Security Risks of Generative AI in Software Testing
- Data Privacy and Security Risks Associated with Using Generative AI
- Data Privacy and Vulnerabilities in Generative AI for Test processes and Tools
- Mitigation Strategies to Protect Data Privacy and Enhance Security in Testing with Generative AI

Energy Consumption and Environmental Impact of Generative AI in Software Testing
- The Impact of Using GenAI on Energy Consumption and CO2 Emissions

AI Regulations, Standards, and Best Practice Frameworks
- AI Regulations, Standards and Frameworks Relevant to GenAI in Software Testing

LLM-Powered Test Infrastructure for Software Testing

Architectural Approaches for LLM-Powered Test Infrastructure
- Key Architectural Components and Concepts of LLM-Powered Test Infrastructure
- Retrieval-Augmented Generation
- The Role of LLM-Powered Agents in Automating Test processes

Fine-Tuning and LLMOps: Operationalizing Generative AI for Software Testing
- Fine-Tuning LLMs for Test tasks
- LLMOps when Deploying and Managing LLMs for Software Testing

Deploying and Integrating Generative AI in Test organizations

Roadmap for the Adoption of Generative AI in Software Testing
- Risks of Shadow AI
- Key Aspects of a Generative AI Strategy in Software Testing
- Selecting LLMs/SLMs for Software Test Tasks
- Phases when Adopting Generative AI in Software Testing

Manage Change when Adopting Generative AI for Software Testing
- Essential Skills and Knowledge for Testing with Generative AI
- Building Generative AI Capabilities in Test Teams
- Evolving Test Processes in AI-Enabled Test organizations