@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://preview.aclanthology.org/ingest-acl/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."
}