Effective, Platform-Independent GUI Testing via Image Embedding and Reinforcement Learning

• URL: http://arxiv.org/abs/2208.09116v2
• Date: Wed, 12 Jun 2024 15:56:52 GMT
• Title: Effective, Platform-Independent GUI Testing via Image Embedding and Reinforcement Learning
• Authors: Shengcheng Yu, Chunrong Fang, Xin Li, Yuchen Ling, Zhenyu Chen, Zhendong Su,
• Abstract summary: We propose PIRLTest, an effective platform-independent approach for app testing. It utilizes computer vision and reinforcement learning techniques in a novel, synergistic manner for automated testing. PILTest explores apps with the guidance of a curiosity-driven strategy, which uses a Q-network to estimate the values of specific state-action pairs.
• Score: 15.458315113767686
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Software applications have been playing an increasingly important role in various aspects of society. In particular, mobile apps and web apps are the most prevalent among all applications and are widely used in various industries as well as in people's daily lives. To help ensure mobile and web app quality, many approaches have been introduced to improve app GUI testing via automated exploration. Despite the extensive effort, existing approaches are still limited in reaching high code coverage, constructing high-quality models, and being generally applicable. Reinforcement learning-based approaches are faced with difficult challenges, including effective app state abstraction, reward function design, etc. Moreover, they heavily depend on the specific execution platforms, thus leading to poor generalizability and being unable to adapt to different platforms. We propose PIRLTest, an effective platform-independent approach for app testing. It utilizes computer vision and reinforcement learning techniques in a novel, synergistic manner for automated testing. It extracts the GUI widgets from GUI pages and characterizes the corresponding GUI layouts, embedding the GUI pages as states. The app GUI state combines the macroscopic perspective and the microscopic perspective, and attaches the critical semantic information from GUI images. This enables PIRLTest to be platform-independent and makes the testing approach generally applicable on different platforms. PIRLTest explores apps with the guidance of a curiosity-driven strategy, which uses a Q-network to estimate the values of specific state-action pairs to encourage more exploration in uncovered pages without platform dependency. The exploration will be assigned with rewards for all actions, which are designed considering both the app GUI states and the concrete widgets, to help the framework explore more uncovered pages.

Related papers

• OS-ATLAS: A Foundation Action Model for Generalist GUI Agents [55.37173845836839]
  OS-Atlas is a foundational GUI action model that excels at GUI grounding and OOD agentic tasks. We are releasing the largest open-source cross-platform GUI grounding corpus to date, which contains over 13 million GUI elements.
  arXiv  Detail & Related papers  (2024-10-30T17:10:19Z)
• Leveraging Large Vision Language Model For Better Automatic Web GUI Testing [7.480576630392405]
  This paper proposes VETL, the first LVLM-driven endtoend web testing technique. With LVLM's scene understanding capabilities, VETL can generate valid and meaningful text inputs focusing on the local context. The selection of associated GUI elements is formulated as a visual question-answering problem, allowing LVLM to capture the logical connection between the input box and the relevant element.
  arXiv  Detail & Related papers  (2024-10-16T01:37:58Z)
• GUICourse: From General Vision Language Models to Versatile GUI Agents [75.5150601913659]
  We contribute GUICourse, a suite of datasets to train visual-based GUI agents. First, we introduce the GUIEnv dataset to strengthen the OCR and grounding capabilities of VLMs. Then, we introduce the GUIAct and GUIChat datasets to enrich their knowledge of GUI components and interactions.
  arXiv  Detail & Related papers  (2024-06-17T08:30:55Z)
• VideoGUI: A Benchmark for GUI Automation from Instructional Videos [78.97292966276706]
  VideoGUI is a novel multi-modal benchmark designed to evaluate GUI assistants on visual-centric GUI tasks. Sourced from high-quality web instructional videos, our benchmark focuses on tasks involving professional and novel software. Our evaluation reveals that even the SoTA large multimodal model GPT4o performs poorly on visual-centric GUI tasks.
  arXiv  Detail & Related papers  (2024-06-14T17:59:08Z)
• Interlinking User Stories and GUI Prototyping: A Semi-Automatic LLM-based Approach [55.762798168494726]
  We present a novel Large Language Model (LLM)-based approach for validating the implementation of functional NL-based requirements in a graphical user interface (GUI) prototype. Our approach aims to detect functional user stories that are not implemented in a GUI prototype and provides recommendations for suitable GUI components directly implementing the requirements.
  arXiv  Detail & Related papers  (2024-06-12T11:59:26Z)
• Practical Non-Intrusive GUI Exploration Testing with Visual-based Robotic Arms [14.3266199543725]
  We propose a practical non-intrusive GUI testing framework with visual robotic arms. RoboTest integrates novel GUI screen and widget detection algorithms, adaptive to detecting screens of different sizes. We evaluate RoboTest with 20 mobile apps, with a case study on an embedded system.
  arXiv  Detail & Related papers  (2023-12-17T09:05:39Z)
• Vision-Based Mobile App GUI Testing: A Survey [29.042723121518765]
  Vision-based mobile app GUI testing approaches emerged with the development of computer vision technologies. We provide a comprehensive investigation of the state-of-the-art techniques on 271 papers, among which 92 are vision-based studies.
  arXiv  Detail & Related papers  (2023-10-20T14:04:04Z)
• NiCro: Purely Vision-based, Non-intrusive Cross-Device and Cross-Platform GUI Testing [19.462053492572142]
  We propose a non-intrusive cross-device and cross-platform system NiCro. NiCro uses the state-of-the-art GUI widget detector to detect widgets from GUI images and then analyses a set of comprehensive information to match the widgets across diverse devices. At the system level, NiCro can interact with a virtual device farm and a robotic arm system to perform cross-device, cross-platform testing non-intrusively.
  arXiv  Detail & Related papers  (2023-05-24T01:19:05Z)
• Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing [66.57888248681303]
  We propose a novel emerging issue detection approach named MERIT. Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version. Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT.
  arXiv  Detail & Related papers  (2020-08-23T06:34:05Z)
• Visual Imitation Made Easy [102.36509665008732]
  We present an alternate interface for imitation that simplifies the data collection process while allowing for easy transfer to robots. We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector. We experimentally evaluate on two challenging tasks: non-prehensile pushing and prehensile stacking, with 1000 diverse demonstrations for each task.
  arXiv  Detail & Related papers  (2020-08-11T17:58:50Z)
• Applied Awareness: Test-Driven GUI Development using Computer Vision and Cryptography [0.0]
  Test-driven development is impractical: it generally requires an initial implementation of the GUI to generate golden images or to construct interactive test scenarios. We demonstrate a novel and immediately applicable approach of interpreting GUI presentation in terms of backend communications. This focus on backend communication circumvents deficiencies in typical testing methodologies that rely on platform-dependent UI affordances or accessibility features.
  arXiv  Detail & Related papers  (2020-06-05T22:46:48Z)

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.