ISTQB Certified Tester - Specialist Level Testing with Generative AI

Pre-requisites

Participants intending to take the ISTQB Certified Tester - Specialist Level Testing with Generative AI examination must hold the Certified Tester Foundation Level certificate. Familiarity with basic software testing concepts and test automation is strongly recommended.

Who should attend

This course is designed for: Testers, Test Analysts, Test Automation Engineers, Test Managers, User Acceptance Testers and Software Developers. Project Managers, Quality Managers, Software Development Managers, Business Analysts, IT Directors and Management Consultants seeking an understanding of GenAI in testing.

Course Objectives

By the end of this course, participants will: Understand fundamental concepts, capabilities and limitations of Generative AI in testing. Develop practical skills in prompt engineering for software test tasks. Identify and mitigate risks of using GenAI, including hallucinations, reasoning errors and data privacy issues. Explore LLM-powered test infrastructure and operational practices (LLMOps, fine-tuning).

Introduction to Generative AI for Software Testing

AI spectrum: Symbolic AI, ML, Deep Learning, GenAI Basics of LLMs, tokenization, embeddings, multimodal models Applications of GenAI in testing (chatbots vs. LLM-powered tools)

Prompt Engineering for Effective Software Testing

Structured prompts: role, context, instruction, input, constraints, output format Core techniques: prompt chaining, few-shot, meta prompting Applying prompts to test analysis, design, automation, monitoring Evaluating and refining prompts using metrics

Managing Risks of Generative AI in Software Testing

Hallucinations, reasoning errors, biases: detection and mitigation Data privacy and security risks; compliance with GDPR and AI regulations Environmental impact and energy considerations AI standards and best practice frameworks (ISO/IEC 42001, EU AI Act)

LLM-Powered Test Infrastructure for Software Testing

Architectural components of LLM-powered test tools Retrieval-Augmented Generation (RAG) Fine-tuning and LLMOps for operationalising GenAI

Deploying and Integrating Generative AI in Test Organisations

Roadmap for adoption; risks of shadow AI Selecting LLMs/SLMs for test tasks Change management: building AI skills, evolving roles and processes