# GUITester: Enabling GUI Agents for Exploratory Defect Discovery

arXiv: https://arxiv.org/abs/2601.04500 · HTML: https://mlsanity.com/paper/2601.04500/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Placed in

- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Builds on (most cited first, 15 of 15)

- [UI-TARS: Pioneering Automated GUI Interaction with Native Agents](https://mlsanity.com/paper/2501.12326/) (2025)
- [AppAgent: Multimodal Agents as Smartphone Users](https://mlsanity.com/paper/2312.13771/) (2023)
- [Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception](https://mlsanity.com/paper/2401.16158/) (2024)
- [OmniParser for Pure Vision Based GUI Agent](https://mlsanity.com/paper/2408.00203/) (2024)
- [Mobile-Agent-v3: Fundamental Agents for GUI Automation](https://mlsanity.com/paper/2508.15144/) (2025)
- [GUI-Actor: Coordinate-Free Visual Grounding for GUI Agents](https://mlsanity.com/paper/2506.03143/) (2025)
- [LLM for Test Script Generation and Migration: Challenges, Capabilities, and Opportunities](https://mlsanity.com/paper/2309.13574/) (2023)
- [GUI Testing Arena: A Unified Benchmark for Advancing Autonomous GUI Testing Agent](https://mlsanity.com/paper/2412.18426/) (2024)
- [AUITestAgent: Automatic Requirements Oriented GUI Function Testing](https://mlsanity.com/paper/2407.09018/) (2024)
- [Vision-Based Mobile App GUI Testing: A Survey](https://mlsanity.com/paper/2310.13518/) (2023)
- [ResTest: Targeted Coverage of Residual Not-Covered Code Using Large Language Models for Web GUI Testing](https://mlsanity.com/paper/2506.00520/) (2025)
- [GUIPilot: A Consistency-based Mobile GUI Testing Approach for Detecting Application-specific Bugs](https://mlsanity.com/paper/2506.07385/) (2025)
- [Can a Chatbot Support Exploratory Software Testing? Preliminary Results](https://mlsanity.com/paper/2307.05807/) (2023)
- [UX Debt: Developers Borrow While Users Pay](https://mlsanity.com/paper/2104.06908/) (2021)
- [Streamlining Acceptance Test Generation for Mobile Applications Through Large Language Models: An Industrial Case Study](https://mlsanity.com/paper/2510.18861/) (2025)

## Built on by (most cited first, 5 of 5)

- [WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing](https://mlsanity.com/paper/2603.25226/) (2026)
- [DiagEval: Trajectory-Conditioned Diagnosis for Reliable Software Evaluation with GUI Agents](https://mlsanity.com/paper/2605.17439/) (2026)
- [Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability](https://mlsanity.com/paper/2605.07110/) (2026)
- [GUITestScape: Towards Open-set Evaluation on Exploratory GUI Testing](https://mlsanity.com/paper/2605.29532/) (2026)
- [GUITAR: Structured Failure Diagnosis of GUI Agents via State Transitions](https://mlsanity.com/paper/2609.34113/) (2026)

## Abstract (arXiv)

Exploratory GUI testing is essential for software quality but suffers from high manual costs. While Multi-modal Large Language Model (MLLM) agents excel in navigation, they fail to autonomously discover defects due to two core challenges: textitGoal-Oriented Masking, where agents prioritize task completion over reporting anomalies, and textitExecution-Bias Attribution, where system defects are misidentified as agent errors. To address these, we first introduce GUITestBench, the first interactive benchmark for this task, featuring 143 tasks across 26 defects. We then propose GUITester, a multi-agent framework that decouples navigation from verification via two modules: (i) a textitPlanning-Execution Module (PEM) that proactively probes for defects via embedded testing intents, and (ii) a textitHierarchical Reflection Module (HRM) that resolves attribution ambiguity through interaction history analysis. GUITester achieves an F1-score of 48.90% (Pass@3) on GUITestBench, outperforming state-of-the-art baselines (33.35%). Our work demonstrates the feasibility of autonomous exploratory testing and provides a robust foundation for future GUI quality assurance~footnoteOur code is now available in~hrefhttps://github.com/ADaM-BJTU/GUITestBenchhttps://github.com/ADaM-BJTU/GUITestBench.