Baris Sarialioglu
TesterYou, TurkeyBarış Sarıalioğlu is a software engineer, digital transformation consultant, author, and international keynote speaker with more than 24 years of experience in software development, quality engineering, agility, product management, and artificial intelligence. He has led global teams and transformation initiatives across industries including banking, telecommunications, automotive, defense, aviation, insurance, and e-commerce. As the Managing Partner of TesterYou, Barış helps organizations bridge technology, business, and human-centered innovation. He has delivered keynote speeches and workshops in more than 50 countries and is a member of the ISTQB General Assembly, actively contributing to the advancement of the global software testing profession. His work combines engineering, science, art, and emerging technologies to challenge conventional thinking about quality, innovation, and the future of software.
AI Testing in Practice: Quality Engineering for Intelligent Systems
Preliminary Outline:
Almudena Daehin
SCRM – Lidl International Hub, SpainAlmudena Vivanco is a seasoned performance engineer with over 20 years of experience in high-traffic systems. Currently, she serves as Principal Performance Engineer at SCRM – Lidl International Hub in Barcelona. Known for her expertise in chaos engineering, she has delivered talks and workshops at various conferences, including PulpoCon and DevOpsCon. Beyond her professional endeavours, Almudena organizes WebPerf BCN and is an active member of the WOPR (Workshop on Performance and Reliability) community. Her passion for mathematics and science, combined with her dynamic approach to system resilience, makes her a prominent figure in the DevOps and performance engineering communities.
K6 Basics in the Age of AI: Introducing MCP-k6 and Building Your k6 Skills
As AI-assisted development reshapes how engineers write, test, and operate software, performance testing is evolving just as rapidly. This workshop introduces the fundamentals of k6 while exploring how AI-powered tooling is transforming the way performance tests are designed, generated, and maintained.
Participants will begin with the core concepts of k6, learning how to create lightweight, script-based performance tests using JavaScript and how to validate system behavior through key metrics such as latency, throughput, and error rates. Building on this foundation, the session introduces mcp-k6, an open-source project that connects k6 with AI agents via the Model Context Protocol (MCP). This enables developers to generate and execute tests using natural language, making performance testing more accessible and iterative.
The workshop also explores copilot-k6-skills, which demonstrates how curated AI "skills" can guide tools like GitHub Copilot to produce more accurate and context-aware k6 scripts. Together, these tools highlight a new workflow where AI assists with test creation while engineers retain control through domain knowledge and validation practices.
Throughout the session, participants will learn how to:
By the end of the workshop, attendees will understand how to combine essential k6 expertise with modern AI-assisted approaches, enabling faster development without compromising the reliability and accuracy of performance testing.
Predrag Skoković
Quality House, SerbiaPredrag Skokovic is a seasoned software developer and tester with over two decades of experience in leading development teams. He holds a degree in Computer Science from the University of Novi Sad, where he also worked as a demonstrator.
Predrag has a proven track record of successfully delivering international projects while promoting early software testing practices. His expertise extends across diverse industries, such as medicine, petrochemistry, finance, and banking.
Predrag is recognized for his contributions to the field of software testing and has been honored as a regular speaker at international conferences, a guest lecturer at the University of Novi Sad, a member of the South East European Testing Board (SEETB), an accredited ISTQB trainer, and a professional consultant.
He co-founded and served as the president of the Board of Test'RS Club, a community of professional software testers. Also, Predrag co-founded Quality House in Serbia, where he holds the position of managing director.
Beyond "Generate Test Cases": From Prompts to Agentic Testing Workflows
Many testers have already tried using AI to generate test cases, automate scripts, review requirements, or explain failures. The first results often look impressive, but trust quickly becomes a problem when the AI invents behavior, produces generic tests, suggests brittle automation, or explains a CI failure with confidence but without evidence.
This tutorial is for testers, test automation engineers, QA leads, and quality engineers who want to use AI in real testing work without losing control of context, risk, evidence, and human judgment.
Instead of treating AI as a one-shot answer machine, the tutorial explores how to design structured AI-assisted testing workflows. Through a realistic product team scenario, participants move from weak prompts to reusable prompt chains, and then from prompt chains to controlled agentic loops where AI can help observe, reason, propose actions, evaluate results, and revise, while testers remain responsible for decisions.
Practical examples include requirement analysis, risk-based test design, test automation review, flaky test investigation, and CI failure triage. Along the way, participants examine where AI helps, where it misleads, and what guardrails are needed to avoid hallucinated requirements, over-generated tests, weak assertions, brittle automation, and false confidence.
Participants leave with reusable workflow templates for AI-assisted test design, automation review, and failure investigation, plus a lightweight canvas for deciding what context AI needs, what it may produce, what evidence it must provide, where humans must review, and when the loop should stop.
Learning objectives:
Why attend:
Attend this tutorial if you have already seen AI produce impressive testing output — but you were not sure how much of it you could trust.
This is not a tutorial about collecting clever prompts. It is about designing practical AI-assisted testing workflows that make context, risk, evidence, uncertainty, and human judgment explicit.
You will not need a specific automation framework or tool stack. The exercises are scenario-based and can be applied whether you work with UI automation, API testing, exploratory testing, CI pipelines, or quality engineering practices.
Szilard Szell and Ard Kramer
Eficode Oy, FinlandSzilárd Széll is a DevOps and AI Transformation Lead, Test Coach, and SAFe 6.0 SPC at Eficode. He has a decade of experience with DevOps transformation, especially in the telco industry. He has also worked as an assessor, trainer, facilitator, and coach in test automation and testing process improvement.
Szilard is very involved in the testing community, and received the Tester of the Year in Finland award in 2024. He runs the Finnish Testing Meetup Group with his friends. He is also active in International Software Testing Qualifications Board (ISTQB) as the Product Owner of the Quality in DevOps Syllabus,. For many years, Szilard has been working on and supporting conferences like HUSTEF, UCAAT, EuroSTAR.
OrangeCrest Consulting, Netherlands
Ard is a principal (quality) consultant from the Netherlands, working at OrangeCrest Consulting. He refers to himself as a "Qualisopher", someone who loves truth and wisdom and is also determined to improve humanity and its environment. This reflects his interest in the world around us: he seeks to learn from it and apply those insights to software testing. His ambition is to contribute, as a dedicated Qualisopher, to a wide range of projects, such as exploring the impact of emerging technologies (like AI). He also takes inspiration from unexpected angles, for example, examining what we can learn from sports to enhance software testing practices.
The change game of quality
In every organization, teams know plenty about what could be improved in quality, testing, and risk management, but the real breakthrough comes from learning how to prioritize wisely and influence effectively. Today’s fast moving technological landscape, especially with AI accelerating change, demands not just technical insight but strategic thinking, persuasion, and the ability to navigate ambiguity.
In this workshop, participants step inside a fictional company where they must decide which improvements truly matter and how to gain support for them. Instead of simply identifying issues, you'll explore how risk appetite, organizational dynamics, and evolving technologies shape what gets attention and what doesn’t.
You and your team will face realistic dilemmas:
Should you strengthen prerequisites like clear requirements? Boost testability? Improve your risk management approach? Or adapt your strategy to the influence of AI? Each choice reflects a different vision of what “quality” means in a modern organization and forces you to re evaluate your own assumptions.
Across multiple iterations, you’ll collaborate on decisions, tackle stakeholder challenges, and respond to shifting contexts. Budget cuts, time pressure, and competing priorities will test your ability to lead change, not just propose it. Along the way, you’ll experience how strategic prioritization, clarity in communicating risk, and understanding technology maturity directly shape your ability to create meaningful impact.
Your facilitator, a seasoned quality coach, will introduce real world constraints and thought provoking questions based on their own lessons and missteps. Through guided reflection and peer discussion, you’ll uncover how different teams approached the same challenges and what that reveals about effective transformation in practice.
By the end of the workshop, you will have:
This session is designed to shift your thinking, from “What needs improvement?” to “How do I shape the environment so improvement actually happens?” And in a world where teams often feel overwhelmed by possibilities, this new perspective is more important than ever.
Join an energetic, scenario driven experience that equips you not jt with ideas for change but with the mindset and methods to make that change real.
Michaël Pilaeten
SOFICO, BelgiumBreaking the system, helping to rebuild it, and providing advice and guidance on how to avoid problems. That's me in a nutshell. With over 20 years of experience in software consultancy in a variety of environments, I have seen the best (and worst) in software development. I'm responsible for guiding consultants, partners, and customers on their personal and professional path towards excellence. I'm chair of the ISTQB Advanced work group, author and international keynote speaker.
First Time Right: More Efficient Test Design, using AI
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.
Erik van Veenendaal
Improve IT Services BV, NetherlandsErik van Veenendaal is a leading international consultant and trainer, and a recognized expert in the area of software testing and quality management. He is the author of a number of books and papers within the profession, one of the core developers of the TMap testing methodology and the TMMi test improvement model. Erik is a frequent keynote and tutorial speaker at international testing and quality conferences. For his major contribution to the field of testing, Erik received the European Testing Excellence Award and the ISTQB International Testing Excellence Award. More information on Erik at www.erikvanveenendaal.nl
Risk-Based Testing for Agile & DevOps teams
Although most projects implicitly use some kind of risk-based approach for prioritizing testing activities, test decisions should be based on a product risk assessment process. Today, quality (including product risk assessment and risk control) is a team responsibility. Risk Poker is a practical method for performing product risk assessments in Agile and DevOps context. Based on practical experiences, It is explained how to carry out team-based risk identification and analyzes, e.g., during refinement sessions, and how to use the outcome to define the test approach. Learn how to apply risk-based testing in both Agile and DevOps context, including creating a one-page sprint "test plan". Practical experiences are shared, problems overcome and results achieved with product risk assessments. Learn how to optimize your test effort using risk-based testing.
You will learn to:
Razvan Vancea
Zitec, RomaniaRazvan Vancea is a Principal QA Engineer, trainer, and content creator with over 10 years of experience in test automation and leadership. Through his YouTube channel – Learn with RV – and blog, Razvan shares his expertise, contributing to the global testing community.
Scratch to Smart: Building a Scalable Playwright Framework with AI Agents and MCP Server
In this hands-on masterclass, we will explore how AI agents can transform the way we build and maintain Playwright test automation frameworks. Using TypeScript and the Playwright MCP Server, you will learn how to integrate Copilot Agents (Planner, Generator, Healer) to accelerate test creation, improve test coverage, and reduce maintenance effort - all while keeping a clean, scalable framework design.
Prerequisites for attendants to install before the session:
Expected level: Beginner
Yehor Maksimchuk
EPAM Systems, UkraineYehor Maksymchuk is a QA Architect, Performance Engineering Team Lead, and QA Engineering Manager with more than 14 years of experience in software quality engineering, test automation, performance testing, and quality strategy.
Throughout his career, Yehor has led QA organizations of up to 22 engineers across multiple Scrum teams, defining testing strategies, building automation frameworks from scratch, and establishing performance engineering practices for large-scale enterprise systems. His experience spans fintech, healthcare, retail, manufacturing, and data analytics domains.
Currently, Yehor leads QA delivery and performance engineering initiatives, driving automation-first approaches, integrating quality gates into CI/CD pipelines, and helping organizations scale testing practices across complex software ecosystems. His work has resulted in significant improvements in test coverage, release efficiency, and production quality.
Yehor specializes in software testing architecture, performance engineering, test automation, quality governance, and modern SDLC transformation. In recent years, he has focused on applying AI technologies to software testing, including AI agent validation, AI-assisted test engineering, and the integration of AI capabilities into software delivery processes.
He is an active mentor, trainer, and open-source contributor, helping engineering teams adopt modern testing practices and prepare for the next generation of AI-enabled software development.
AI Agent Testing and Building an AI-Driven SDLC
As organizations increasingly adopt AI-powered applications and AI agents, software testing teams face new challenges in quality assurance, risk management, and governance. Traditional testing approaches are often insufficient for validating systems that rely on large language models, autonomous decision-making, and non-deterministic behavior.
This tutorial explores practical approaches for testing AI agents and integrating AI capabilities into the Software Development Life Cycle (SDLC). Participants will learn how QA and engineering teams can establish effective validation strategies, quality controls, and governance practices while accelerating delivery through AI-assisted engineering.
Key topics include:
