You have invested years into building a sophisticated test automation framework. But as product evolved, the once-robust suite started showing its age.

We faced this exact problem. What was meant to accelerate testing became a heavy burden—duplicate tests piled up, flaky results eroded confidence, minor UI or API changes broke dozens of scripts, and feedback cycles slowed to a crawl.

During one intense meeting, a Gen-Z team member made a bold statement: “These tests should think, communicate, and self-organize.” That thought became the turning point.

In this presentation, we reveal how we navigated the crisis, emerged stronger with a robust framework powered by AI agents by sharing our journey from a rigid automation framework to a multi-agent testing ecosystem. We believe MACT represents not just evolution—but a leap forward in making “Testing at its Best” intelligent, distributed, and future-ready.

What you will learn

1. How Multi-Agent Cooperative Testing (MACT) was designed and implemented to create intelligent, adaptive, and resilient automation through collaborative test agents
2. How moving beyond traditional automation to context-aware, adaptive execution improve overall test effectiveness?
3. Learn how smarter orchestration speed execution, reduce maintenance, and boost client collaboration?

The Lufthansa Group established a global Test Factory to provide consistent, high-quality testing services across a wide range of projects and technologies. Built and operated together with TestSolutions GmbH, this Test Factory has become a central element in ensuring structured, efficient, and innovative quality assurance throughout the organization.

In this talk, we will share practical insights into how such a large-scale Test Factory is set up and operated. We will introduce the organizational structure, governance approach, and collaboration models that enable seamless integration with multiple business units. The focus will be on the lessons learned from establishing a standardized yet flexible model that supports diverse project needs.

We will illustrate these experiences with selected examples from current focus areas, such as test data management, accessibility testing, scaled agile testing, and the testing of and with AI. These cases highlight how the Test Factory addresses emerging challenges while maintaining efficiency and quality across projects.

What you will learn

1. Key building blocks of a global Test Factory
2. Effective practices for operating at scale
3. Practical lessons from Lufthansa’s Test Factory

Are you ready to rethink what quality means in the age of AI?

As artificial intelligence becomes embedded in everything from customer support to healthcare, finance, and product recommendations, the expectations for software quality are rapidly changing. Traditional testing methods fall short when applied to AI systems, which are probabilistic, data-driven, and often opaque.

This session is designed for Quality Engineers and Managers who are ready to take the lead in shaping how we ensure the reliability, fairness, and safety of AI-powered applications.

Drawing on real-world examples and nearly 20 years of hands-on experience, I will walk through the strategies, frameworks, and tools needed to evolve your QE practice for the AI era.

What you will learn

1. Learn practical methods to test AI systems for accuracy, fairness, robustness, and interpretability
2. Discover tools and frameworks to monitor, validate, and govern AI behavior across the ML lifecycle
3. Gain a roadmap to evolve your quality engineering role and processes for AI-powered applications

Join Michael Bolton, co-author of Taking Testing Seriously, as he explores the AI Productivity Paradox: the faster software is created, the more pressure teams face to skip the critical thinking needed to assess quality and risk.

“AI will take your job”
We keep hearing this but is this actually true?

The short answer: sometimes.
The better answer: that might be exactly what we want.

Building AI models — and building real products on top of them — turns out to be mostly about testing. Not just checking if software works, but validating behavior, bias, reliability, edge cases, and trust at scale. In other words, quality has moved from the end of the pipeline to the center of everything ALLL the way from the time you decide to do an AI project!

In this keynote, we’ll cut through the hype to look at what’s actually happening in AI today — not science fiction, not nonsense promises, but the real systems being shipped into production. We’ll explore how AI is fundamentally changing what “software quality” means, and why testers and quality engineers are uniquely positioned to lead this shift.

You’ll see practical ways testers can start building AI skills right now and apply them directly to current projects — even if you’ve never trained a model before. We’ll also talk honestly about which testing tasks AI should take over, and which new responsibilities are emerging in their place.

Most importantly, this talk challenges testers to stop reacting to the future and start designing it — becoming the role models who define what quality, trust, and accountability look like in an AI-driven world.

AI may take your testing job. But you’ll have a MUCH better one instead.

What you will learn

1. What does this have to do with software quality
2. How testers and quality engineers can build up their AI-skills and use AI for their current projects
3. How testers can figure out what their jobs will be in 5 years…and be the role models to lead us there.

AI is changing the foundations of software testing. Traditional approaches built around predictable behavior, fixed expected results, and simple pass/fail outcomes are no longer enough.