@inproceedings{gao-etal-2026-guitester,
title = "{GUIT}ester: Enabling {GUI} Agents for Exploratory Defect Discovery",
author = "Gao, Yifei and
Wu, Jiang and
Chen, Xiaoyi and
Yang, Yifan and
Cui, Zhe and
Ma, Tianyi and
Zhang, Jiaming and
Sang, Jitao",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.946/",
pages = "18956--18978",
ISBN = "979-8-89176-395-1",
abstract = "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: Goal-Oriented Masking, where agents prioritize task completion over reporting anomalies, and Execution-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 Planning-Execution Module (PEM) that proactively probes for defects via embedded testing intents, and (ii) a Hierarchical 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."
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<abstract>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: Goal-Oriented Masking, where agents prioritize task completion over reporting anomalies, and Execution-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 Planning-Execution Module (PEM) that proactively probes for defects via embedded testing intents, and (ii) a Hierarchical 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.</abstract>
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%0 Conference Proceedings
%T GUITester: Enabling GUI Agents for Exploratory Defect Discovery
%A Gao, Yifei
%A Wu, Jiang
%A Chen, Xiaoyi
%A Yang, Yifan
%A Cui, Zhe
%A Ma, Tianyi
%A Zhang, Jiaming
%A Sang, Jitao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F gao-etal-2026-guitester
%X 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: Goal-Oriented Masking, where agents prioritize task completion over reporting anomalies, and Execution-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 Planning-Execution Module (PEM) that proactively probes for defects via embedded testing intents, and (ii) a Hierarchical 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.
%U https://aclanthology.org/2026.findings-acl.946/
%P 18956-18978
Markdown (Informal)
[GUITester: Enabling GUI Agents for Exploratory Defect Discovery](https://aclanthology.org/2026.findings-acl.946/) (Gao et al., Findings 2026)
ACL
- Yifei Gao, Jiang Wu, Xiaoyi Chen, Yifan Yang, Zhe Cui, Tianyi Ma, Jiaming Zhang, and Jitao Sang. 2026. GUITester: Enabling GUI Agents for Exploratory Defect Discovery. In Findings of the Association for Computational Linguistics: ACL 2026, pages 18956–18978, San Diego, California, United States. Association for Computational Linguistics.