aiqbench analysis
RotDroid leverages a fine-tuned vision-language model to solve the non-crashing functional failures caused by screen rotation in Android apps, marking a significant application of AI in automated software testing. By generating semantically equivalent GUI states across orientations, this framework demonstrates how machine learning can bridge the gap between portrait and landscape view validation where traditional oracles fail. The approach successfully identified numerous previously unknown bugs, highlighting the practical utility of AI-driven methods for improving mobile app reliability.
RotDroid: Cross-Orientation State Equivalence Testing for Detecting GUI Rotation Bugs in Android Apps
arXiv:2608.25425v1 Announce Type: cross 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.
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.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2608.25425 [cs.SE]
(or arXiv:2608.25425v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2608.25425 Focus to learn more arXiv-issued DOI via DataCite
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