Conference Programme

Quality is often treated as something that happens after development, owned by QA, supported by automation, and reviewed only when defects escape. But in modern engineering teams, this model breaks quickly. When delivery speed increases, architecture becomes more distributed, releases become more frequent, and AI-assisted development accelerates code creation, quality cannot remain a downstream testing responsibility. It becomes a leadership system.

This Interactive workshop with short teaching blocks, group exercises, leadership reflection, and practical artifacts will helps the attendees understand how quality is shaped by everyday decisions: how teams are structured, how requirements are clarified, how architecture is reviewed, how risks are surfaced, how automation is used, how incidents are learned from, and how leaders create shared accountability. The goal is not to turn engineering leaders into QA experts. The goal is to help them lead quality intentionally.

The workshop is ideal for Engineering Managers, Tech Leads, Engineers, QA Leads, Delivery Managers, Product Owners working closely with engineering, Architects responsible for technical direction and Agile leaders responsible for delivery predictability.

Most test design techniques — equivalence partitioning, boundary value analysis, decision tables, state transition testing — are taught as step-by-step procedures for picking values. Treated this way, they feel mechanical and get abandoned once the course ends. This workshop reframes them as modelling tools: ways of building a picture of the software's behaviour so that coverage, gaps and risk become visible, not just values to plug into a table.

We start from a simple idea: testing is a learning activity. We learn from specifications, but also from examples and from direct experience of the system under test — and a good tester deliberately uses all three. The session explores the distinction between requirements, examples and tests, then works through domain testing, state transition testing, decision tables and Gherkin-style scenarios as modelling techniques rather than recipes.

Participants build models directly from a set of provided requirements and derive a first set of tests from them — setting up the models and test ideas that Part 2 puts into practice against a real system.

Format: presentation, discussion and hands-on modelling exercises.

AI agents have existed for decades, but generative AI has fundamentally changed what agents can do and how they are designed and built. Come and explore the evolution of AI agents across two major waves. First, learn the foundations of Agentic AI through agents built using rules, heuristics, and traditional machine learning, examining where these approaches excel and why they struggle with complexity, ambiguity, and scale. Then dive into the second wave of agents powered by generative AI and multimodal large language models. These modern agents can reason, plan, use tools, and interact with diverse data sources, enabling far more adaptive and autonomous behavior.

Join Tariq King as he helps you build and experiment with AI agents trained for various tasks, and gain the experience needed to extend them via Retrieval Augmented Generation (RAG), reasoning, and emerging standards such as the Model Context Protocol (MCP). By the end of the session, you will understand how AI agents have evolved and be ready to deploy them to help you automate everything from individual tasks to entire workflows.

You know that tester on your team, the one who's sharp as hell, always prepared, deeply thoughtful… and somehow ends up being the least heard voice in the room? They sit at the table, listen attentively, draft the perfect comment in their head and by the time they're ready to share it, the moment has passed. They walk out frustrated, quietly convinced they're not cut out for the next level. This workshop is about why that happens and what to do about it, whether you're the invisible one or the leader responsible for making sure your best people aren't. Testers often operate in the shadows, believing their work will become visible on its own. But it doesn't. Your work needs a voice, and that voice can only come from you.

Most testers are taught to focus on the work, find the bugs, write the tests, and protect quality. Nobody teaches them how to make that work visible or how to position themselves for growth. The result is that capable testers get overlooked, not because they lack skill, but because they lack voice.

This is getting more urgent, not less. As AI reshapes what testing looks like, the testers who will thrive are those who can articulate their value beyond execution and show how their thinking, judgment, and ownership drive business outcomes. The ones who stay silent and hope to be noticed will find themselves increasingly vulnerable. This workshop matters because it addresses the gap between doing great work and being recognised for it; a gap that holds back careers and quietly drains motivation across testing teams everywhere.

I'll share my personal journey of rapid career growth and the strategies that helped me scale my impact and value tenfold. I'll walk through the mindset shift from execution to full ownership, the tactics and differentiators that accelerated my career, and practical ways to align your technical work with business outcomes so that your contribution becomes impossible to overlook. You'll walk away knowing how to make your value visible, position yourself for growth, and stay competitive in a fast-changing tech landscape, whether that change is driven by AI or anything else.

Your AI tool can generate test code in seconds. But can your team actually use it well? Speed is not maturity. The real question is: can your team give the right instructions, verify what AI produces, and improve continuously? The teams that gain a true edge are not the ones who adopted the fastest tool — they are the ones who learned to use it wisely. That gap between knowing about AI-driven testing and applying it with judgment is exactly what this workshop addresses.

In this half-day, hands-on session, you will work through a structured, three-part programme designed to move your team from curiosity to concrete action.

Part 1: Diagnose where you stand Using a lightweight self-assessment, you will measure your team across four dimensions of AI-era QA maturity: 1) The readiness of your test environment and data 2) The quality of the inputs you give to AI (clarity, accuracy, and sufficiency) 3) How well your team verifies AI-generated outputs 4) How clearly the roles and responsibilities between your team and AI agents are defined In 20 to 30 minutes, you will have a clear picture of where your team's maturity stands and where the bottleneck is.

Part 2: See it in action, step by step Rather than asking AI to "just write the tests," we demonstrate a structured flow from test analysis and design through to implementation and execution, where each step has a defined input, a defined output, and a human review before moving forward. This step-by-step approach makes it clear where Human-in-the-Loop matters and prevents test coverage from slipping. We also show how an AI agent can take over part of that review, such as checking whether your test viewpoints are complete. A public sample repository is provided: participants with a laptop can follow along hands-on; those without can observe and map every step directly to their own team's context.

Part 3: Leave with a plan Based on your diagnosis and the live demonstration, you will identify three concrete actions your team can start the very next day, tailored to your weakest area and not a generic checklist.

This workshop is vendor-neutral and tool-agnostic. The methods apply regardless of which AI platform your team uses. It is designed for QA engineers, test leads, and engineering managers who want practical, proven guidance on building a team that truly knows how to work with AI.

AI is changing how software teams work. It can generate code, create test cases, summarize requirements, analyze logs, produce documentation, and speed up many repetitive activities. But this creates a dangerous misconception: that people are becoming less important. In reality, people are becoming more important. AI can produce answers, but it cannot fully understand business context, customer emotion, ethical consequences, team dynamics, organizational risk, or the hidden trade-offs behind engineering decisions. Without strong human judgment, AI can make teams faster at producing weak decisions.

This interactive workshop explores why critical thinking, empathy, and team judgment are now essential skills in AI-driven software delivery. Attendees will work through realistic exercises involving AI-generated requirements, test cases, bug analysis, release decisions, and customer-impact scenarios. They will learn how to question assumptions, detect shallow reasoning, challenge tool output, think from the customer’s perspective, and build team-level decision intelligence. The workshop is not about rejecting AI. It is about using AI responsibly, intelligently, and humanely. Attendees will leave with practical tools to improve critical thinking in their teams, run better review conversations, ask stronger questions, and ensure that AI supports human intelligence rather than replacing it.

Core Workshop Idea In the AI era, teams are being pushed to move faster. Code can be generated faster. Test cases can be drafted faster. Documentation can be summarized faster. Logs can be analyzed faster. But speed without thinking creates a new risk: teams may start accepting AI output without questioning assumptions, context, customer impact, ethical risk, maintainability, or long-term consequences. This workshop is designed to help engineering, QA, product, and leadership teams understand that AI does not reduce the need for people. It increases the need for better people thinking. The central message is: AI can accelerate output, but only people can provide judgment. The workshop focuses on strengthening critical thinking, empathy, questioning skills, team reasoning, and decision quality. Attendees will work through practical exercises where AI-generated outputs look correct but contain hidden risks, missing context, shallow assumptions, or customer-impact blind spots.

This workshop is ideal for QA Leads, Testers, Developers, Tech Leads, Product Owners, Scrum Masters, Delivery Managers, Senior Engineers, AI adoption champions and anyone involved in AI-assisted software delivery

Test design doesn't stop once test cases are written — the most useful information often surfaces once a tester actually gets their hands on the system. This session puts test design into action: participants register on a purpose-built web application and test it using prepared tests derived from requirements, alongside deliberate, unscripted exploratory testing.

Working from a shared briefing document and the requirements, the group runs scripted tests, captures exploratory test sessions, and hunts specifically for "other things to test" that the requirements never mentioned. We compare what each approach finds, discuss when to plan and when to explore, and log the bugs uncovered along the way.

The session closes on coverage, effectiveness and reporting: how do we know when we've tested enough, and how do we communicate what we found?

Builds on the models and requirements from Part 1, but a short recap means testers with test design experience can join as a standalone practical session.

Workshop participants will need a laptop, tablet or mobile phone to register on a specially written application to be tested. There is also a dedicated app to be used to capture participants notes, test cases, test results and bug reports.

Format: hands-on testing, exploratory session and group debrief.

Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security, and operations.

Join Tariq King as he gives you a quality engineering introduction to testing AI and machine learning. You’ll learn AI and ML fundamentals, including how intelligent agents are modelled, trained and developed. Tariq then dives into approaches for validating ML models offline, prior to release, and online, continuously post-deployment. Engage with other attendees to develop and execute a test plan for a live ML-based recommendation system, and experience the practical issues around testing AI first-hand. Tariq wraps up the tutorial with a set of expert-recommended, AI engineering practices to help your organization develop trusted machine learning systems.

Have you wondered what AI was meant to change for testers and what we got instead? We asked AI to help with testing. We got more test cases - faster. That is not help. That is the same factory with a faster engine. For seventy years, the testing profession has produced glorified clerical work — documentation, scripts, reports, maintenance - at the cost of the work that actually matters. Risk analysis. Critical evaluation. Deep exploration. Asking the right question. After years of watching teams up close, I put the split at roughly 70/30 — clerical work dominating, the thinking that creates value getting the leftovers. State of Testing™ 2026 reports 76.8% AI adoption, and the wrong side keeps getting bigger.

If we look up-close, the quality is not a property of software alone. It lives in the product the team builds, in the people who build it, and in the project around them. Quality Conscious Software Delivery (QCSD) calls these the three notions of quality — the QualiTri model. None of them moves without the others. But delivering on that requires intellectually demanding way of working, time to breath and think through, a luxury that very few teams can afford. That is what this workshop is about - flipping the math on your own laptop, against your own project.

We work with Agentic QCSD through five agent swarms that span the SDLC — Ideation, Refinement, Development, Verification, Production - built on an open-source, MIT licensed framework. The swarms do not run your testing for you. They do not pair with your designers, and they do not replace your judgement. What they do is take the work that drains every testability session, every QX conversation, every design review: the analyses, the scores, the product-factor maps that thinking testers know they should bring but rarely have time to prepare. When the team finally sits down to decide, the gaps are already mapped. The testability is already scored. The factors are already on the board. Pairing time goes to thinking, not finding. Phase walls come down. Role seams soften. The clerical 70% goes to the swarms. The thinking 30% - the reason you became a tester - gets room to breathe, and reach further than one mind ever could on its own.

Four hours. Hands-on. You bring a laptop and a real project. You leave with the framework running on it, and a plan for Monday.

Automation is evolving rapidly, and AI is changing how testers design, create, and maintain automated tests.

Starting with the fundamentals, you will learn about Playwright architecture, browser automation concepts, locators, assertions, and Page Object Models. The workshop then advances into AI-assisted automation techniques, showing how modern AI coding assistants can generate Playwright scripts, enhance test coverage, explain failures, and help testers become more productive faster.

Through hands-on exercises, you will build test scripts, explore advanced features such as locators, fixtures, and debugging, and discover how AI can simplify repetitive tasks, generate test cases, and improve productivity.

Whether you are brand new to automation or looking to level up, you will leave with working tests, proven patterns, and an AI-assisted workflow you can use from day one. No prior Playwright experience required.