RotDroid: Cross-Orientation State Equivalence Testing for Detecting GUI Rotation Bugs in Android Apps

• URL: http://arxiv.org/abs/2608.25425v1
• Date: Wed, 26 Aug 2026 06:30:21 GMT
• Title: RotDroid: Cross-Orientation State Equivalence Testing for Detecting GUI Rotation Bugs in Android Apps
• Abstract summary: RotDroid is a testing framework for detecting GUI rotation bugs via cross-orientation state equivalence.<n>RotVL is a vision-language model fine-tuned for equivalence checking.<n>In large-scale studies on open- and closed-source apps, RotDroid reports 94 previously unknown bugs, with 47 confirmed or fixed by developers.
• Score: 4.7807641576596165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• GraphDroid: Asynchronous LLM-Based Mobile App GUI Testing via History-Aware Exploration and Hybrid Intent Fulfillment [15.320789851146543]
  GraphDroid is an intent-driven GUI testing framework that integrates a cluster-based memory mechanism.<n>We evaluate GraphDroid on 41 real-world Android apps against six state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-09-09T11:04:53Z)
• Embedding Rotation Invariance for Provable Multi-Oriented Scene Text Recognition [49.665177053925184]
  Multi-oriented text is a major challenge for scene text recognition.<n>Existing rotation-aware methods explicitly estimate text orientation.<n>We incorporate rotation invariance into the STR framework to address these limitations.
  arXiv  Detail & Related papers  (2026-08-11T09:06:46Z)
• DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners? [57.585275546688116]
  Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents.<n>We argue that choosing when and where to spend test-time compute is central to bringing frontier performance to the real world.<n>We introduce DIRECT, a routing framework that uses multimodal scene context to allocate compute per prompt.
  arXiv  Detail & Related papers  (2026-06-10T17:58:49Z)
• Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models [82.10380665213867]
  Can vision-language models predict what a 180 rotation would reveal from the original image alone?<n>We study this ability through Rotated-Outcome Prediction.<n>Current vision-language models can recognize a transformed visual state when it is shown, but often fail to predict that state from the original view.
  arXiv  Detail & Related papers  (2026-06-01T14:16:09Z)
• Seeing is Believing: Vision-driven Non-crash Functional Bug Detection for Mobile Apps [26.96558418166514]
  This paper proposes a novel vision-driven, multi-agent collaborative automated GUI testing approach for detecting non-crash functional bugs.<n>We evaluate Trident on 590 non-crash bugs and compare it with 12 baselines, it can achieve more than 14%-112% and 108%-147% boost in average recall and precision.
  arXiv  Detail & Related papers  (2024-07-03T11:58:09Z)
• RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments [55.864869961717424]
  It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. We design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these problems.
  arXiv  Detail & Related papers  (2023-10-23T16:30:39Z)
• NBMOD: Find It and Grasp It in Noisy Background [15.959994864858958]
  We propose a large-scale grasp detection dataset called NBMOD: Noisy Background Multi-Object for grasp detection. This paper presents a Rotation Anchor Mechanism (RAM) to address this issue. Considering the high real-time requirement of robotic systems, we propose RA-GraspNet (GraspNet with Rotation Anchor): RARA (network with Rotation Anchor and Region Attention), RAST (network with Rotation Anchor and Semi Transformer), and RAGT (network with Rotation Anchor and Global Transformer)
  arXiv  Detail & Related papers  (2023-06-17T05:56:05Z)
• AlphaRotate: A Rotation Detection Benchmark using TensorFlow [63.39088942989411]
  AlphaRotate is an open-source benchmark for performing scalable rotation detection on various datasets. It currently provides more than 18 popular rotation detection models under a single, well-documented API. AlphaRotate can be installed from PyPI and is released under the Apache-2.0 License.
  arXiv  Detail & Related papers  (2021-11-12T11:56:40Z)
• Towards Rotation Invariance in Object Detection [6.235602417651423]
  We show that the de-facto method for bounding box label rotation, the Largest Box Method, creates very large labels. We propose a new method of rotation augmentation that can be implemented in a few lines of code.
  arXiv  Detail & Related papers  (2021-09-28T04:44:54Z)
• Attentive Rotation Invariant Convolution for Point Cloud-based Large Scale Place Recognition [11.433270318356675]
  We propose an Attentive Rotation Invariant Convolution (ARIConv) in this paper. We experimentally demonstrate that our model can achieve state-of-the-art performance on large scale place recognition task when the point cloud scans are rotated.
  arXiv  Detail & Related papers  (2021-08-29T09:10:56Z)
• Adjoint Rigid Transform Network: Task-conditioned Alignment of 3D Shapes [86.2129580231191]
  Adjoint Rigid Transform (ART) Network is a neural module which can be integrated with a variety of 3D networks. ART learns to rotate input shapes to a learned canonical orientation, which is crucial for a lot of tasks. We will release our code and pre-trained models for further research.
  arXiv  Detail & Related papers  (2021-02-01T20:58:45Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.