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Traditional script-based testing, like those using Appium, often struggles with frequent UI changes, causing tests to break and requiring engineers to spend 30-40% of their time fixing them rather than developing features. At Uber’s scale—operating in thousands of cities, supporting 55 languages, and running countless experiments—manual testing is infeasible. These limitations make automated testing a necessity, but legacy methods are too brittle to handle the dynamic nature of modern apps.

DragonCrawl addresses this challenge by leveraging generative AI to create tests that adapt to UI changes rather than breaking. Once trained, it can be deployed across all Uber applications without frequent retraining, significantly reducing maintenance overhead. Despite its capabilities, DragonCrawl is highly efficient, requiring only a fraction of the computational resources of large models like ChatGPT, making it cost-effective to run at scale. It works by extracting view hierarchies, determining possible actions, and selecting the best one while dynamically adapting to unexpected pop-ups or variations in UI. By separating execution and validation, it ensures reliable results while minimizing test fragility.

AI-powered assertions further enhance test reliability, moving beyond brittle text or ID checks. Instead, testers can ask high-level questions like “Is there an ad on this screen?” or “Is alcohol visible?” using a generalized visual question-answering (VQA) framework. DragonCrawl’s impact is already significant, catching over 30 critical bugs (each potentially saving Uber millions), resolving 100+ localization issues, and even aiding in language rollouts like Pashto and Dari for Afghan refugees. Additionally, OmegaCrawl is transforming Uber’s internal workflows by automating repetitive operational tasks for developers, reducing inefficiencies.

More broadly, the evolution of AI in testing and development reflects a larger trend in AI research—balancing supervised learning (SL) and reinforcement learning (RL). Early AI models relied heavily on SL, which became too rigid, leading to the rise of RL for adaptability. However, RL alone often results in AI exploiting reward functions, requiring human intervention. The field is now shifting toward a hybrid approach, using SL for structure, RL for adaptability, and operator-driven AI for real-world grounding. The future of AI in software testing and beyond lies in this balance, where models can learn, adapt, and execute tasks while staying aligned with real-world constraints.

Asking AI agents to one-shot apps and hoping their models have enough training to generate the right code was a temporary phase. AutoMobile brings the exact platform specific context for mobile engineers to explore their apps and source code simultaneously while modernizing how we test them.

AutoMobile is a comprehensive set of tools including an MCP that enables AI agents to interact with mobile devices and comes with automated test authoring, AI supported test recovery and drop-in support for test execution. I’ll be walking through some of the development journey and system design with live demos to illustrate the capabilities along the way. I’d like to show you what all of this can do and how tools like this can change the perspective on what’s possible.

Tired of glacial Maven build times? We were, too. Slashing build times in a large 1400 module project is a deliberate journey. This session reveals our multi-year effort to optimize our developer experience. This session isn’t just about the destination; it’s about the critical milestones and unexpected hurdles we encountered along the way.

We’ll share how we used build performance insights to identify and fix critical bottlenecks, navigated challenges during a major Java upgrade, adapted to the performance characteristics of new developer hardware, and implemented caching mechanisms to achieve dramatically faster feedback loops. Learn practical strategies and key considerations for significantly improving your build speeds and boosting developer productivity, regardless of your specific tech stack.

At Intuit, we’re helping developers move faster and smarter by transforming how we build, test, and deploy software. It starts with GenAI-powered failure analysis that reduces time-to-diagnosis from hours to minutes, and continues with a shift toward automated promotions across environments, GitHub Actions, and Argo-powered progressive delivery with built-in auto rollbacks.

By reducing friction in debugging and improving deployment confidence, we’ve already made meaningful gains in developer efficiency. Now, we’re leaning into a bold future—aiming to double deployment frequency and slash lead time for change by simplifying CI and enhancing the intelligence of our promotion pipelines.

This talk walks through the evolution of our CI/CD platform, the developer pain points we tackled, the cultural shifts that made it all stick, and the future ideal state of our platform. If you’re scaling Developer Experience in a complex org, you’ll walk away with actionable insights—proven strategies your engineers will thank you for!

Atlassian platform teams are responsible for driving cross-cutting engineering efforts to increase developer productivity and uplift engineering health. This means those teams working on developer productivity have a unique reliance on other teams to ensure the adoption of their platform capabilities – getting code changes into other teams’ codebases is critical to their success.

However, we heard through our quarterly DevProd survey that there was an organizational chasm between the product and platform teams, resulting in the product teams being inundated with many requests that often came in sideways without clarity on the actual ask from the platform teams. To be effective in improving developer productivity, we needed to become more effective in communicating with engineering teams across the org and give them clear visibility into upcoming priorities so they can plan ahead.

Join us to learn how Atlassian bridged the gap between platform and product teams by using campaigns as the unit of change to effectively scope, target, and communicate changes across hundreds of engineering teams and thousands of services to sustainably drive productivity improvements.