# DragonCrawl: A Generative, Intent-Based Framework for Scalable Mobile End-to-End Testing

arXiv: https://arxiv.org/abs/2607.28750 · HTML: https://mlsanity.com/paper/2607.28750/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 30 Jul 2026.

## Builds on (most cited first, 7 of 7)

- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [DroidBot-GPT: GPT-powered UI Automation for Android](https://mlsanity.com/paper/2304.07061/) (2023)
- [Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing](https://mlsanity.com/paper/2212.04732/) (2022)
- [Owl Eyes: Spotting UI Display Issues via Visual Understanding](https://mlsanity.com/paper/2009.01417/) (2020)
- [Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing](https://mlsanity.com/paper/2311.08649/) (2023)
- [Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning](https://mlsanity.com/paper/2309.01519/) (2023)
- [Towards Test Generation from Task Description for Mobile Testing with Multi-modal Reasoning](https://mlsanity.com/paper/2504.15917/) (2025)

## Abstract (arXiv)

As mobile applications grow in complexity, traditional End-to-End (E2E) testing frameworks struggle with UI volatility, maintenance overhead, and cross-platform scalability. This paper presents DragonCrawl, an AI-driven mobile testing system for continuous regression testing that has evolved from embedding-based similarity matching to generative intent-based reasoning using large language models. Unlike prior LLM-based testing research focused on exploratory testing and crash detection, DragonCrawl validates specific user flows on every code change, blocking commits that break critical functionality. By leveraging GPT-4o's multimodal capabilities, DragonCrawl achieves 91.6% pass rate on iOS and 92.2% on Android across 1,013 automated tests running continuously in CI/CD pipelines. The system reduces test onboarding time from 96-120 hours to under 4 hours and has saved an estimated 27 developer years in test maintenance effort. We present the architectural evolution from V1 (semantic embedding matching) to V2 (generative intent-based reasoning), discuss implementation challenges including token explosion and memory constraints, and report operational experience from production deployment. The integration of multimodal vision for end-state detection and tool calling for backend state transitions enables comprehensive regression testing that bridges UI interactions with system state. Our results demonstrate that AI-driven testing can maintain stability while eliminating the brittleness of traditional automated tests, enabling continuous quality assurance at scale.