SPONSOR TRACK TALK

The QA Renaissance: How Generative AI, Data-driven Testing and Chaos Engineering Are Reshaping QA

Artificial Intelligence (AI) and Machine Learning (ML) are no longer experimental concepts in QA — they are becoming integral to how we build, validate, and sustain software quality at scale. Generative models can create tests autonomously, synthetic data can simulate user behavior with precision, and AI-enhanced observability can uncover failure signals long before they reach users.

This talk explores how modern QA teams are blending these advanced technologies with chaos engineering practices to build smarter, more resilient systems. Through a mix of theory, open-source innovation, and practical implementation, we’ll present real-world scenarios that highlight the evolving role of QA in an AI-powered development lifecycle.

· Generative AI for Test Design: Use models like Llama and GPT-4.x to generate tests from user stories or requirements and integrate them into frameworks like Robot Framework or Playwright.

· Synthetic Data for Smarter Testing: Leverage open-source tools like Synthetic Data Vault (SDV) to create high-fidelity, privacy-preserving test datasets that mirror real-world behavior.

· AI-Driven Observability: Apply Prometheus, Grafana, and anomaly detection models (e.g., Facebook Prophet) to proactively monitor and detect subtle performance issues and regressions.

· Intelligent Chaos Engineering: Use Litmus Chaos or Chaos Mesh with machine learning models to dynamically generate failure scenarios based on historical outages or user patterns.

What you’ll learn

Session details

Diogo Goncalves Candeias

With more than 10 years in the Research & Development area, Diogo has a comprehensive and competitive knowledge about Architecture & Quality Assurance areas in software development with special focus in Automation (Robot Process Automation, Machine Learning and automation playbooks) and Artificial Intelligence. Biomedical Scientific R&D is also one of his interests.