Nordic Testing Days 2026

Artificial Intelligence is increasingly used to write test code. Today it is estimated that 60% of new test code is written by AI tools and this number will only continue to rise. Surprisingly, however, the testing is now the critical component needed is to ‘test’ the 40% that AI is unable to test itself, that is where the future of testers.

That 40% is not busy work. This is not your typical motivational speech dressed up as another AI testing keynote. What I will talk about in the keynote is the three critical capabilities that are becoming more valuable not less, and the underlying reason for this is that all of them depend on something that current models lack: the human intent behind engineering quality software for other humans to use.

• Engineer the Context Understanding: While AI can greatly amplify the effectiveness of testing, the quality of results is only as good as the input you give it. In 2026, the testers who will rise to the top will not be the ones who came up with the cutest prompt for the AI to answer. Rather, they will be those who can successfully engineer the context of the problem for the AI to solve. We will look at real-world best practices for testing with AI, and I will share an AI Assurance playbook for context engineering that will immediately raise the quality of your AI-infused testing.
• Review with Heuristic Judgment: AI generated test suites look good - until they fail to test what really matters silently. Green pipelines are the most dangerous artefact in organisations today. No one asks: what are we not testing? This capability helps organisations audit test suites generated by AI with a sense of heuristic judgement. I can explain how to design world class AI augmented tests like an experienced navigator reading a map to identify the blank spaces where organisation specific risks reside.
• Orchestrate Trust: Humans trust humans they still need decide when the machine is wrong. That is not a technical skill - it is a quality leadership act. This capability describes the shifting role of quality engineers to trust AI Assurance oracles. This capability explores the new role of orchestrating AI Assurance that trust across teams, tools, and stakeholders, and answers the question: “Who is really responsible for quality?”

By the end of this keynote, you will have an assessment of your skills in planning, architecting, designing and leading AI governance, a practical plan of action to implement in the real world, and the assurance that you will be “ready” to become part of the “Irreplaceable 40%”.

Key Takeaways:

• Context Engineering is our New Superpower: AI-augmented tests are only as valuable as the context a human provides. Attendees will leave with a repeatable framework for curating system, user, and business context that transforms AI output from "technically correct" into "intent driven tests" - a skill that compounds in value as the important of testing of AI infused systems improve.

• Heuristic Auditing Catches the Real Truth: A passing automated test regression suite is not proof of quality; it is proof that the tests you wrote passed. Attendees will gain practical heuristic-led auditing patterns to interrogate value-driven testing, identify dangerous blind spots, and ask the questions that thinking machines are structurally incapable of asking themselves.

• Trust Orchestration is the Quality Engineer's Next Career: The highest-value skill in an AI-augmented team is not technical testing - it is the ability to calibrate, communicate, and own confidence decisions across people, tools, and stakeholders. Attendees will understand how to position themselves as the trust architect oracles in their organisation increasingly needs, turning a perceived threat into their most durable career advantage.

As organizations rush to adopt Large Language Models (LLMs), many discover that building reliable, trustworthy applications is far from straightforward. Unlike traditional software, LLM outputs are non-deterministic, context-dependent, and vulnerable to issues like bias, hallucinations, and prompt injection. Ensuring quality requires more than testing—it demands a holistic approach that blends architecture, safety, observability, and continuous feedback. This talk explores practical strategies for embedding quality into LLM-powered systems from the ground up. We’ll cover methods for prompt design, evaluation frameworks, guardrails, and hybrid architectures that improve accuracy and safety. Attendees will leave with a clearer understanding of how to balance innovation with reliability and how to design AI applications that are not only powerful but also consistent, secure, and user-focused.


Key takeaways:

1. Testing LLMs requires new methods, not just old QA practices.Combine automation + human oversight for best results.
2. Build feedback and safety into the system from the start.
3. Quality is a continuous journey, not a release milestone.

Pull requests, aka merge requests, are a goldmine of information that many testers are missing out on. Although pull requests are often seen as a tool mainly for developers, they offer testers an equal opportunity to provide feedback. When joining the review process testers can contribute to the software quality before changes are added to the codebase. They can use their unique perspective to uncover insights that go beyond the code. They will learn more about the bigger picture of the application. And ultimately, testers can apply these observations to their testing and help the team prevent bugs.In this talk, Andrea will introduce how testers can get involved in the pull request review process. She will demonstrate how testers can shift left and proactively engage with the source code changes. The step-by-step approach covers everything from checking policies and standards. It also includes looking at build pipelines, code changes, and unit tests. Starting with the basics, the session gives clear examples at every stage, making it easy to follow along.Andrea will highlight practical examples and insights drawn from real-life experiences, providing actionable tips on how to get the most out of pull request reviews. The session illustrates how testers can analyze pull requests for quality, learn more about the source code, and make a real impact through their feedback.At the end of the session, attendees will have a hands-on guide to reviewing pull requests. They will be ready to take a closer look at their team’s work and discover new insights, just as curious koalas explore branches to find tasty leaves of eucalyptus. It’s time for testers and their teams to realise the untapped potential of making pull request reviews a shared responsibility.


Key takeaways:

1. Understand the elements of a pull request and know about the basic principles of the Git workflow
2. Learn how to shift-left and contribute to the pull request review process
3. By the end of the session, participants will be able to start reviewing pull requests

Intro:Artificial Intelligence challenges almost every assumption the testing discipline is built on. Traditional testing depends on fixed inputs and predictable logic, but AI systems are adaptive, probabilistic, and context-dependent. That means our classical test cases are no longer stable reference points.


In this 20-minute talk, Nicole van Gijn explores what testing looks like when your system learns, reasons, and occasionally hallucinates. She introduces the AI Quality Grid, a structured framework co-developed with John Kronenberg, that helps define quality attributes, risks, and validation strategies for AI applications. The session bridges theory and practice through concrete examples from a real AI test project, showing how LLM-evals and risk-based thinking can be combined to test prompt robustness, output consistency, and bias control within modern CI/CD pipelines.


Attendees will walk away with a lightweight but actionable structure for AI quality assessment and a new mindset: understanding quality not as a checklist, but as an intelligent, adaptive discipline. Why this topic is relevant:

1. AI systems are rapidly entering production pipelines, yet testing methods lag behind.
2. Testers and QA leads urgently need practical models to evaluate non-deterministic outputs.
3. The AI Quality Grid offers a bridge between AI model evaluation (LLM-evals) and classical test strategy, providing testers with new tools and thinking patterns to stay relevant in the AI era.

Deploying to production shouldn't require a meeting, three approvals, and a prayer. Yet most teams treat every release like launching a rocket - mission control on standby, everyone watching the countdown, but no one wanting to press the red button.

At Sokos Hotels, our web booking system handles thousands of reservations daily, processing millions monthly across 50 hotels all over Finland and Estonia. One critical bug means lost revenue; one outage means thousands of unhappy guests. We were trapped in weekly releases, manual verification, and the kind of Thursday tension that put everyone in the mission control room. We asked ourselves if Continuous Deployment is just a myth.

In less than a year, we broke the cycle. We went from weekly manual releases to deploying seven times per day with higher confidence than ever. The results? 4.5/5.0 customer effort score, 40% higher conversion rates and 4.3+/5.0 overall team happiness. The secret? A testing strategy that made deployment boring for the last 2 years.

Join my talk to find out how we did it!

What you’ll get from this talk:

1. The three signs your team is not ready for Continuous Deployment, and the technical enabler that breaks the testing tension. 
2. How we shifted from “QA signs off on releases” to "Quality is built-in", and why the cultural change was harder than the technical one. 
3. The valuable failure lessons we learned along the way and what it taught us about green pipeline.

Your takeaway: A practical, battle-tested roadmap for testing-enabled Continuous Deployment. You'll leave knowing which tests to automate first, how to build confidence without sacrificing speed, and how to prove to skeptics that this isn't just another risky experiment. Real patterns, real failures, real results - ready to implement Monday morning.

Who should attend: QA engineers, test automation engineers, developers, engineering managers, and DevOps practitioners who believe testing should accelerate delivery, not slow it down.

The problem: it is too hard to understand and improve GenAI quality, and yet organizations are moving ahead regardless.For AI engineers it’s hard to:- Increase accuracy due to lack of repeatable & representative testing- Understand reliability: know how, why, or when an agent will failThis leads to poor reliability and accuracy, which:- Increases operational costs and can increase reputational damage- Erodes user trust, reduces customer engagement, and increases churn- Reduces business confidence, slowing down AI adoptionIn this talk I will discuss the limitations of how we are current testing AI agents, and why this means we are not adequately ensuring the safety of agentic AI systems. With non-deterministic systems like Generative/Agentic AI, we need to simulate a large number of inputs (millions) and measure the outputs using judge agents to find the statistical success rate. This a process that is more similar to how we traditionally do load testing rather than the simple functional testing we’re using with AI right now. I will explain how you can instead use tools like AgentCore to create orchestration agents that build other types of agent to make this new type of non-deterministic testing possible.This approach will be for GenAI what traditional automated tests are for deterministic code- Auto generate representative testing material- Orchestrate tests against real AI endpoints- Judge outputs (minimum standards, accuracy quantification)- Improve accuracy and reliability


Key takeaways:

1. Current functional testing techniques are inadequate for testing agentic/generative AI systems
2. What does it mean to use LLM as Judge agents? What are input agents?
3. How can you create an AI testing orchestration pipeline for testing AI agents

Most of us unknowingly carry limiting beliefs about our abilities, often in the very areas where we have the most room to grow! A talented developer won't apply for a senior role because they think “I'm just not a natural leader”, never considering that leadership can be learned. Or a designer brushes off feedback believing, "Either you have an eye for design or you don't." Sound familiar?These are not signs of missing innate talent, but rather the result of a fixed mindset, a belief that your innate talent is set in stone. The good news is that this belief can be changed by adopting a growth mindset, a belief that you are capable of growing and improving. Drawing on insights from Dr. Carol S. Dweck’s pioneering research, this talk explores how a fixed mindset makes us avoid challenges and crumble at criticism, while a growth mindset helps us see both as fuel for improvement. I'll break down the real difference between fixed and growth mindset and we'll do an interactive exercise where you'll experience the shift yourself.The best part? Your potential isn't fixed. You will walk away with a practical strategy to stop protecting your ego and start building the skills necessary to get that senior role and tackling the challenges you've been avoiding.


Key takeaways:

Participants will leave this session with:

1. An understanding of how we overestimate our knowledge
2. A clear distinction between fixed mindset and growth mindset
3. Practical strategies for adopting a growth mindset

Every few years, software testing gets a new existential threat. Outsourcing was going to end QA careers. So was automation. Now it's AI. The doomsday headlines feel new each time, but the pattern stays the same. You’re met with the same anxiety and the same conversations with peers wondering if QA careers have a future. The problem is that we treat disruption as destruction, when in reality it’s an opportunity to reinvent yourself. And the people who do that well often share the same set of qualities. In this keynote, Kristel Kruustük draws on 15 years in software testing and her experience as a founder to uncover the mindset, habits, and decisions that have kept testers relevant through every wave of change and will continue to do so when the next wave hits.

Key Takeaways: 

• Why disruption in software testing is a pattern, not a one-time event
• Why the "AI will replace us" story misses what's actually happening in the industry
• Real examples of companies and roles that have come back stronger after changes
• How to build resilience as a practical skill, not an abstract trait
• What to do in the next 6 to 12 months to position yourself for what comes next