Vision-driven Automated Mobile GUI Testing via Multimodal Large Language Model

• URL: http://arxiv.org/abs/2407.03037v1
• Date: Wed, 3 Jul 2024 11:58:09 GMT
• Title: Vision-driven Automated Mobile GUI Testing via Multimodal Large Language Model
• Authors: Zhe Liu, Cheng Li, Chunyang Chen, Junjie Wang, Boyu Wu, Yawen Wang, Jun Hu, Qing Wang,
• Abstract summary: This paper proposes a vision-driven automated GUI testing approach to detect non-crash functional bugs with Multimodal Large Language Models. It begins by extracting GUI text information and aligning it with screenshots to form a vision prompt, enabling MLLM to understand GUI context. VisionDroid identifies 29 new bugs on Google Play, of which 19 have been confirmed and fixed.
• Score: 27.97964877860671
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: With the advancement of software rendering techniques, GUI pages in mobile apps now encompass a wealth of visual information, where the visual semantics of each page contribute to the overall app logic, presenting new challenges to software testing. Despite the progress in automated Graphical User Interface (GUI) testing, the absence of testing oracles has constrained its efficacy to identify only crash bugs with evident abnormal signals. Nonetheless, there are still a considerable number of non-crash bugs, ranging from unexpected behaviors to misalignments, often evading detection by existing techniques. While these bugs can exhibit visual cues that serve as potential testing oracles, they often entail a sequence of screenshots, and detecting them necessitates an understanding of the operational logic among GUI page transitions, which is challenging traditional techniques. Considering the remarkable performance of Multimodal Large Language Models (MLLM) in visual and language understanding, this paper proposes a vision-driven automated GUI testing approach VisionDroid to detect non-crash functional bugs with MLLM. It begins by extracting GUI text information and aligning it with screenshots to form a vision prompt, enabling MLLM to understand GUI context. The function-aware explorer then employs MLLM for deeper and function-oriented GUI page exploration, while the logic-aware bug detector segments the entire exploration history into logically cohesive parts and prompts the MLLM for bug detection. We evaluate VisionDroid on three datasets and compare it with 10 baselines, demonstrating its excellent performance. The ablation study further proves the contribution of each module. Moreover, VisionDroid identifies 29 new bugs on Google Play, of which 19 have been confirmed and fixed.

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