Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI
Testing
- URL: http://arxiv.org/abs/2311.08649v1
- Date: Wed, 15 Nov 2023 01:59:40 GMT
- Title: Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI
Testing
- Authors: Juyeon Yoon, Robert Feldt and Shin Yoo
- Abstract summary: 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.
- Score: 17.24045904273874
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: GUI testing checks if a software system behaves as expected when users
interact with its graphical interface, e.g., testing specific functionality or
validating relevant use case scenarios. Currently, deciding what to test at
this high level is a manual task since automated GUI testing tools target lower
level adequacy metrics such as structural code coverage or activity coverage.
We propose DroidAgent, an autonomous GUI testing agent for Android, for
semantic, intent-driven automation of GUI testing. It is based on Large
Language Models and support mechanisms such as long- and short-term memory.
Given an Android app, DroidAgent sets relevant task goals and subsequently
tries to achieve them by interacting with the app. Our empirical evaluation of
DroidAgent using 15 apps from the Themis benchmark shows that it can set up and
perform realistic tasks, with a higher level of autonomy. For example, when
testing a messaging app, DroidAgent created a second account and added a first
account as a friend, testing a realistic use case, without human intervention.
On average, DroidAgent achieved 61% activity coverage, compared to 51% for
current state-of-the-art GUI testing techniques. Further, manual analysis shows
that 317 out of the 374 autonomously created tasks are realistic and relevant
to app functionalities, and also that DroidAgent interacts deeply with the apps
and covers more features.
Related papers
- AUITestAgent: Automatic Requirements Oriented GUI Function Testing [12.83932274541321]
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.
arXiv Detail & Related papers (2024-07-12T06:14:46Z) - CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents [52.83132876539399]
Crab is the first benchmark framework designed to support cross-environment tasks.
Our framework supports multiple devices and can be easily extended to any environment with a Python interface.
The experimental results demonstrate that the single agent with GPT-4o achieves the best completion ratio of 35.
arXiv Detail & Related papers (2024-07-01T17:55:04Z) - GUI Odyssey: A Comprehensive Dataset for Cross-App GUI Navigation on Mobile Devices [61.48043339441149]
GUI Odyssey consists of 7,735 episodes from 6 mobile devices, spanning 6 types of cross-app tasks, 201 apps, and 1.4K app combos.
We developed OdysseyAgent, a multimodal cross-app navigation agent by fine-tuning the Qwen-VL model with a history resampling module.
arXiv Detail & Related papers (2024-06-12T17:44:26Z) - Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration [52.25473993987409]
We propose Mobile-Agent-v2, a multi-agent architecture for mobile device operation assistance.
The architecture comprises three agents: planning agent, decision agent, and reflection agent.
We show that Mobile-Agent-v2 achieves over a 30% improvement in task completion compared to the single-agent architecture.
arXiv Detail & Related papers (2024-06-03T05:50:00Z) - DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language Model [90.71963723884944]
Text-to-image (T2I) generative models have attracted significant attention and found extensive applications within and beyond academic research.
We introduce DiffAgent, an agent designed to screen the accurate selection in seconds via API calls.
Our evaluations reveal that DiffAgent not only excels in identifying the appropriate T2I API but also underscores the effectiveness of the SFTA training framework.
arXiv Detail & Related papers (2024-03-31T06:28:15Z) - 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) - Make LLM a Testing Expert: Bringing Human-like Interaction to Mobile GUI
Testing via Functionality-aware Decisions [23.460051600514806]
GPTDroid is a Q&A-based GUI testing framework for mobile apps.
We introduce a functionality-aware memory prompting mechanism.
It outperforms the best baseline by 32% in activity coverage, and detects 31% more bugs at a faster rate.
arXiv Detail & Related papers (2023-10-24T12:30:26Z) - Agents: An Open-source Framework for Autonomous Language Agents [98.91085725608917]
We consider language agents as a promising direction towards artificial general intelligence.
We release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience.
arXiv Detail & Related papers (2023-09-14T17:18:25Z) - AutoDroid: LLM-powered Task Automation in Android [32.241570727243534]
We introduce AutoDroid, a mobile task automation system capable of handling arbitrary tasks on any Android application without manual efforts.
The main components include a functionality-aware UI representation method that bridges the UI with the LLM.
We evaluate its performance on a new benchmark for memory-augmented Android task automation with 158 common tasks.
arXiv Detail & Related papers (2023-08-29T13:02:30Z) - 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) - DroidBot-GPT: GPT-powered UI Automation for Android [11.980924738484994]
DroidBot-GPT is a tool that utilizes GPT-like large language models (LLMs) to automate the interactions with Android mobile applications.
Given a natural language description of a desired task, DroidBot-GPT can automatically generate and execute actions that navigate the app to complete the task.
arXiv Detail & Related papers (2023-04-14T11:31:56Z)
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.