Test Design techniques have been around for decades  - see Glenford Myers "The art of software testing" from 1979 - and are continuously revised and updated. They became mainstream and part of international standards (ISTQB, IEEE, ISO). Where their existence is known (things like Equivalence Partitioning, Boundary Value Testing & Decision Table Testing are considered fundamental knowledge for testers), their efficiency and effectiveness is less addressed.

With AI-assisted testing (vibe testing), we have a great companion to translate requirements into test conditions, test cases and test suites. But how do we check whether these are the right test cases? That there are no blind spots? How to avoid overlap? And how do we ensure that we selected the best technique for a given situation?

This workshop will give a high level overview of some of the most common test techniques, compare and contrast them, and help you applying them in your prompts

Artificial Intelligence (AI) is redefining the role of software testing within the Software Development Life Cycle (SDLC), shifting it from a traditionally reactive activity to a proactive, intelligent, and continuous process. AI-driven testing introduces automation at scale, enabling faster, more accurate, and more adaptive quality assurance practices.

In test design, AI can analyze requirements and user behavior to automatically generate relevant and high-coverage test cases. During test execution, intelligent automation tools enhance efficiency through capabilities such as self-healing test scripts, dynamic element identification, and smart test prioritization. AI also enables predictive analytics, allowing teams to identify high-risk areas, detect defects earlier, and focus testing efforts where they matter most.