NTD2025: Landseer Enga - Revolutionizing Mobile Testing with Generative AI at Scale скачать в хорошем качестве

NTD2025: Landseer Enga - Revolutionizing Mobile Testing with Generative AI at Scale

In this deep-dive technical session, I'll share how Uber revamped its mobile testing approach using large language models (LLMs) to create DragonCrawl - a system that tests mobile applications with human-like intuition. I'll walk through how we transformed mobile testing from a maintenance-heavy, script-based approach to an intelligent, adaptive system that can handle UI changes automatically across multiple languages and cities. The session will cover: The challenges of traditional mobile testing at scale (3,000+ simultaneous experiments) Architecture and implementation of DragonCrawl using MPNet and embedding techniques Real-world examples of DragonCrawl's adaptive behavior and problem-solving capabilities Practical strategies for handling LLM challenges like hallucinations and adversarial cases Results and metrics from production deployment Live demonstration of DragonCrawl in action Key Takeaways: Understanding how to leverage LLMs for automated testing, including model selection criteria, architecture decisions, and implementation strategies that enable human-like testing behavior Practical techniques for handling LLM challenges in production systems, including specific approaches to manage hallucinations, adversarial cases, and edge scenarios Real-world insights into scaling automated testing across multiple languages and locations without maintaining separate test scripts, including specific metrics and benchmarks from Uber's production environment