RotDroid: Cross-Orientation State Equivalence Testing for Detecting GUI Rotation Bugs in Android Apps
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Abstract (arXiv)
Screen rotation is a fundamental interaction in Android applications, but it often introduces non-crashing functional failures (NCFs), such as layout inconsistencies and state loss, which are difficult to detect automatically. A key challenge is the lack of effective test oracles for checking cross-orientation state equivalence between portrait and landscape views. We propose RotDroid, a testing framework for detecting GUI rotation bugs via cross-orientation state equivalence. RotDroid generates and mutates State-Preserving action Sequences (SPS) to construct semantically equivalent GUI states across orientations. To support reliable oracle checking, we build RotBench, a dataset of paired portrait-landscape GUI states, and develop RotVL, a vision-language model fine-tuned for equivalence checking. Experiments on both synthetic and real-world datasets show that RotVL outperforms state-of-the-art models, and RotDroid detects more rotation-induced failures than existing techniques under equal budgets. In large-scale studies on open- and closed-source apps, RotDroid reports 94 previously unknown bugs, with 47 confirmed or fixed by developers, demonstrating its practical effectiveness.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 36 references that are in the map. First posted on arXiv 26 Aug 2026.
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References · 13
- 2025Qwen3-VL Technical Reportcited by 2,793
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- 2022Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testingcited by 36
- 2023Chatting with GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testingcited by 20
- 2021Deep Reinforcement Learning for Black-Box Testing of Android Appscited by 10
- 2022Nighthawk: Fully Automated Localizing UI Display Issues via Visual Understandingcited by 6
- 2020Data Loss Detector: Automatically Revealing Data Loss Bugs in Android Appscited by 2
- 2022Effective, Platform-Independent GUI Testing via Image Embedding and Reinforcement Learningcited by 2
- 2022The Metamorphosis: Automatic Detection of Scaling Issues for Mobile Appscited by 2
- 2024A Study of Using Multimodal LLMs for Non-Crash Functional Bug Detection in Android Appscited by 2
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