Workshops (Dag 1)

🏛️Spor 1 | 🎤Track 1

Kl.9.00 (Half-day) | In English

Test Planning Explained

By Predrag Skokovic from Quality House

Abstract:

Testing needs a good structure, because in that way we become more efficient, effective and, above all, transparent about the activities and their results. Creating a test plan ‘forces’ us to think carefully in advance about all the important details related to testing the software we’re working on, as well as to anticipate potential risks that may arise.

In this workshop we will discuss the following topics: software test models, test process in context, test planning vs test plan, test strategy and test approach, risks, and many more. Most importantly, we’ll exercise on a real-life example how to plan for test activities and to write it down in a test plan.

During the tutorial we will reference ISO 29119 - software testing standard, in order to guide our thoughts and actions, as any other relevant standard or framework would do.

Learning Objectives:

You will learn:

  • To distinguish between test planning and a test plan, understanding the unique value each brings in effective software testing.

  • To apply structured approaches to test planning, including selecting appropriate test models, defining strategies, and identifying risks.

  • To develop a practical, context-aware test plan, tailored to real-life project constraints, goals, and team dynamics.

  • To reference, incorporate, and adapt industry standards, such as ISO 29119, to guide planning decisions and ensure transparency and alignment.

Session Outline:

  • Part I - theory:

    - Introduction to workshop

    - Software testing models

    - Test process in context

    - Test planning process

    - Test plan

  • Part II - hands-on exercise through collaboration and discussion:

    - Exercise: Write a test plan for Sea Swimming Contest Software System

  • Part III - workshop closure

Target Audience:

Intermediate (basic understanding of the topic)

🏛️Spor 1 | 🎤Track 1

Kl.13.30 (Half-day) | In English

Test Automation Strategies with Design Thinking

By Barış Sarıalioğlu from TesterYou

Abstract:

In an era where speed and quality define competitive advantage, test automation is no longer just a technical practice—it is a strategic capability. Yet, many automation initiatives fail to deliver sustainable value because they focus on tools and frameworks, overlooking the human, creative, and iterative dimensions of problem-solving.

This workshop explores how Design Thinking—with its emphasis on empathy, ideation, and experimentation—can be applied to create robust and adaptable test automation strategies. First we reframe automation challenges from the perspective of diverse stakeholders, ensuring that the solutions align with real user and business needs. Then, we move through ideation techniques to generate innovative approaches for architecture, maintainability, and integration across the delivery pipeline.

You will learn to prototype automation solutions quickly, validate them in realistic scenarios, and iterate based on feedback—transforming automation from a one-time implementation into a continuously evolving ecosystem.

You will gain practical tools and a mindset to build technically sound and future-ready automation strategies.

Learning Objectives:

You will learn:

  • Human-Centered Automation Planning – How to frame automation goals by understanding the real needs of users, testers, and business stakeholders.

  • Creative Problem-Solving Techniques – Using Design Thinking ideation methods to generate innovative automation approaches beyond traditional tool-driven thinking.

  • Rapid Prototyping of Automation Solutions – Applying quick, low-risk experiments to validate automation ideas before full-scale implementation.

  • Sustainable Maintainability Practices – Strategies for designing automation architectures that remain adaptable and cost-effective over time.

  • Iterative Improvement Mindset – How to continuously refine and evolve automation strategies using feedback loops and performance metrics

Session Outline:

  • Introduction & Context

  • Empathize: Understanding the Problem Space

  • Define & Ideate: Shaping the Strategy

  • Prototype & Test: From Idea to Reality

  • Wrap-Up & Key Takeaways

Target Audience:

Advanced (hands-on experience with the topic)

🏛️Spor 2 | 🎤Track 2 

Kl.9.00 (Full-day) | In English

Building an AI-Powered Testing Tools Platform

By Vivianne Guimarães and Tiago Gomes from Thoughtworks

Abstract:

In this hands-on workshop, you will learn how to create a fully functional, AI-powered platform that streamlines software testing and quality assurance. The workshop bridges the gap between AI technologies and real-world software development, and is designed to streamline software testing and quality assurance processes.

In the workshop we will integrate tools such as a test case generator, automated test case executor, and quality strategy generator. By the end of the session, you will have a deployable application that you can share with your teams or organizations, showcasing the power of AI-driven automation.

This workshop has already been held at Nordic Testing Days 2025 with great success.

Learning Objectives:

You will learn:

  • Practical skills to design a customizable platform suited to real-life scenarios

  • To build a full-stack platform

  • To integrate AI-powered tools

  • To deploy and share the application.

Session Outline:

  • Introduction to AI in software testing

  • Overview of AI-powered tools for testing and quality assurance.

    Use cases and benefits of integrating AI into workflows.

  • Setting up the development environment and initializing a React frontend and Express backend.

    Connecting the frontend and backend.

  • Building the test case generator

    Integrating an AI model to create test cases based on user input.

    Designing the UI for test case generation.

  • Creating the automated test case executor

    Implementing logic to execute generated test cases.

    Displaying execution results in the UI.

  • Developing the quality strategy generator

    Using AI to analyze test results and generate quality improvement strategies.

    Visualizing strategies in the application.

  • Deploying the application

    Preparing the application for deployment.

    Deploying to a cloud platform and sharing it with others.

Target Audience:

Intermediate (basic understanding of the topic)

This workshop is suitable for those with a technical mindset. Please bring a computer with installation permissions to actively participate.

🏛️Spor 3 | 🎤Track 3 

Kl.9.00 (Full-day) | In English

AI Meets QA: A Full-Day Journey Through Testing, Tools, and Techniques

By Kari Kakkonen from Gofore

Abstract:

This workshop is a taste of the full ISTQB® AI Testing (CT-AI) 4-day course, with exciting AI and testing knowledge and exercises. The course is an introduction to the topic that lowers the threshold for learning more, perhaps taking a full course or self-studying for an ISTQB CT-AI exam.

Testers encounter the need to test systems that include AI elements more and more. Due to the nature of AI, all traditional test approaches and techniques do not work. Instead AI-specific techniques are needed.

We will cover AI and machine learning basics, AI-specific quality criteria, the testing done with teaching a machine learning model, and after teaching, test levels, approaches, and techniques for testing AI.

We will also cover how AI can help with testing. We will go through how you test AI-based systems and what the challenges are for self-learning systems. We will show how to you use AI to improve your testing.

Learning Objectives:

You will learn:

  • How to use AI-related quality characteristics for better testing

  • To use test techniques to deal with typical AI challenges

  • Situations in your everyday testing where they can utilize AI

Session Outline:

  • Introduction to AI

    Exercise: Machine Learning with Teachable Machine

  • Quality Characteristics for AI Systems

    Exercise: Selecting Objectives and Acceptance Criteria

  • Testing AI-Based Systems

    Exercise: Explainability with ExpliClas

  • Techniques for the Testing of AI

    Exercise: Pairwise testing with Pairwiser

  • Techniques for the Testing of AI

    Exercise: Metamorphic testing

  • Techniques for the Testing of AI

    Exercise: Exploratory testing with TensorFlow

  • Using AI for Testing

    Exercise: GenAI testing

Target Audience:

Basic (no prior knowledge)