Javier Re

Claudia Badell

Activity in Spanish

How to make better decisions when automating? In this masterclass, we'll rethink automation as part of a quality strategy, focusing on context, risks, and teamwork. We'll review effective practices, common mistakes, and criteria for defining what to automate, when, and why, looking beyond scope and frameworks.

Nicolás Paez

Activity in Spanish

Pipelines are at the core of CI/CD practices and are fundamental to any development team's delivery process. Currently, there are a wide variety of tools for implementing them, each with its own features, advantages, and limitations. However, beyond the specifics of each tool, there are common principles that underpin pipeline design that are often overlooked. At the same time, not all organizations are the same, but there are certain patterns—reusable and proven solutions—that can serve as a guide for designing and implementing pipelines more simply. In this workshop, we will review the key principles for pipeline design and explore how different tools address them. We will also analyze the most common challenges organizations face when implementing them and present some patterns that can help us overcome these obstacles effectively.

Tariq King

Activity in English

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 modeled, trained and developed. Tariq then dives into approaches for validating ML models offline, prior to release, and online, continuously post-deployment. Engage with other participants 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.

Dionny Santiago

Activity in English

AI has been rapidly changing the way we approach software testing. Traditional test automation is time-consuming to create and breaks down easily in the presence of change. Fortunately, AI is helping testing teams create less procedural, more resilient tests that are able to self-heal in the presence of modern, rapidly changing, highly dynamic production systems. This sounds great, but you may be asking yourself: How do I get started? What additional skills do I need to learn? What tools are available for me to start using, right now? Join Dionny Santiago as he breaks down different AI techniques and applies them to different testing problems. Discover freely available AI-driven test automation tooling that will help you start building AI-first test automation today without writing a single line of code. Want to tackle the hardest of challenges, and want to learn how to generate new test cases? We will also cover open source tools that can help you build your own neural networks for tackling tough testing problems. Let's build and execute real AI-first tests. No prior programming or AI/ML experience needed!

Javier Re