AUITestAgent: Automatic Requirements Oriented GUI Function Testing

• URL: http://arxiv.org/abs/2407.09018v1
• Date: Fri, 12 Jul 2024 06:14:46 GMT
• Title: AUITestAgent: Automatic Requirements Oriented GUI Function Testing
• Authors: Yongxiang Hu, Xuan Wang, Yingchuan Wang, Yu Zhang, Shiyu Guo, Chaoyi Chen, Xin Wang, Yangfan Zhou,
• Abstract summary: This paper introduces AUITestAgent, the first automatic, natural language-driven GUI testing tool for mobile apps. It is capable of fully automating the entire process of GUI interaction and function verification. Experiments on customized benchmarks demonstrate that AUITestAgent outperforms existing tools in the quality of generated GUI interactions.
• Score: 12.83932274541321
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Graphical User Interface (GUI) is how users interact with mobile apps. To ensure it functions properly, testing engineers have to make sure it functions as intended, based on test requirements that are typically written in natural language. While widely adopted manual testing and script-based methods are effective, they demand substantial effort due to the vast number of GUI pages and rapid iterations in modern mobile apps. This paper introduces AUITestAgent, the first automatic, natural language-driven GUI testing tool for mobile apps, capable of fully automating the entire process of GUI interaction and function verification. Since test requirements typically contain interaction commands and verification oracles. AUITestAgent can extract GUI interactions from test requirements via dynamically organized agents. Then, AUITestAgent employs a multi-dimensional data extraction strategy to retrieve data relevant to the test requirements from the interaction trace and perform verification. Experiments on customized benchmarks demonstrate that AUITestAgent outperforms existing tools in the quality of generated GUI interactions and achieved the accuracy of verifications of 94%. Moreover, field deployment in Meituan has shown AUITestAgent's practical usability, with it detecting 4 new functional bugs during 10 regression tests in two months.

Related papers

• GUI-Bee: Align GUI Action Grounding to Novel Environments via Autonomous Exploration [56.58744345634623]
  We propose GUI-Bee, an MLLM-based autonomous agent, to collect high-quality, environment-specific data through exploration. We also introduce NovelScreenSpot, a benchmark for testing how well the data can help align GUI action grounding models to novel environments.
  arXiv  Detail & Related papers  (2025-01-23T18:16:21Z)
• UI-TARS: Pioneering Automated GUI Interaction with Native Agents [58.18100825673032]
  This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions. In the OSWorld benchmark, UI-TARS achieves scores of 24.6 with 50 steps and 22.7 with 15 steps, outperforming Claude (22.0 and 14.9 respectively)
  arXiv  Detail & Related papers  (2025-01-21T17:48:10Z)
• GUI Testing Arena: A Unified Benchmark for Advancing Autonomous GUI Testing Agent [24.97846085313314]
  We propose a formalized and comprehensive environment to evaluate the entire process of automated GUI Testing. We divide the testing process into three key subtasks: test intention generation, test task execution, and GUI defect detection. It evaluates the performance of different models using three data types: real mobile applications, mobile applications with artificially injected defects, and synthetic data.
  arXiv  Detail & Related papers  (2024-12-24T13:41:47Z)
• Falcon-UI: Understanding GUI Before Following User Instructions [57.67308498231232]
  We introduce an instruction-free GUI navigation dataset, termed Insight-UI dataset, to enhance model comprehension of GUI environments. Insight-UI dataset is automatically generated from the Common Crawl corpus, simulating various platforms. We develop the GUI agent model Falcon-UI, which is initially pretrained on Insight-UI dataset and subsequently fine-tuned on Android and Web GUI datasets.
  arXiv  Detail & Related papers  (2024-12-12T15:29:36Z)
• ShowUI: One Vision-Language-Action Model for GUI Visual Agent [80.50062396585004]
  Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. We develop a vision-language-action model in digital world, namely ShowUI, which features the following innovations. ShowUI, a lightweight 2B model using 256K data, achieves a strong 75.1% accuracy in zero-shot screenshot grounding.
  arXiv  Detail & Related papers  (2024-11-26T14:29:47Z)
• 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)
• Gamified GUI testing with Selenium in the IntelliJ IDE: A Prototype Plugin [0.559239450391449]
  This paper presents GIPGUT: a prototype of a gamification plugin for IntelliJ IDEA. The plugin enhances testers' engagement with typically monotonous and tedious tasks through achievements, rewards, and profile customization. The results indicate high usability and positive reception of the gamification elements.
  arXiv  Detail & Related papers  (2024-03-14T20:11:11Z)
• CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation [61.68049335444254]
  Multimodal large language models (MLLMs) have shown remarkable potential as human-like autonomous language agents to interact with real-world environments. We propose a Comprehensive Cognitive LLM Agent, CoCo-Agent, with two novel approaches, comprehensive environment perception (CEP) and conditional action prediction (CAP) With our technical design, our agent achieves new state-of-the-art performance on AITW and META-GUI benchmarks, showing promising abilities in realistic scenarios.
  arXiv  Detail & Related papers  (2024-02-19T08:29:03Z)
• Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing [17.24045904273874]
  We propose DroidAgent, an autonomous GUI testing agent for Android. It is based on Large Language Models and support mechanisms such as long- and short-term memory. DroidAgent achieved 61% activity coverage, compared to 51% for current state-of-the-art GUI testing techniques.
  arXiv  Detail & Related papers  (2023-11-15T01:59:40Z)
• You Only Look at Screens: Multimodal Chain-of-Action Agents [37.118034745972956]
  Auto-GUI is a multimodal solution that directly interacts with the interface. We propose a chain-of-action technique to help the agent decide what action to execute. We evaluate our approach on a new device-control benchmark AITW with 30$K$ unique instructions.
  arXiv  Detail & Related papers  (2023-09-20T16:12:32Z)
• Chatting with GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testing [23.460051600514806]
  We propose GPTDroid, asking Large Language Model to chat with the mobile apps by passing the GUI page information to LLM to elicit testing scripts. Within it, we extract the static context of the GUI page and the dynamic context of the iterative testing process. We evaluate GPTDroid on 86 apps from Google Play, and its activity coverage is 71%, with 32% higher than the best baseline, and can detect 36% more bugs with faster speed than the best baseline.
  arXiv  Detail & Related papers  (2023-05-16T13:46:52Z)

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.