Make LLM a Testing Expert: Bringing Human-like Interaction to Mobile GUI
Testing via Functionality-aware Decisions
- URL: http://arxiv.org/abs/2310.15780v1
- Date: Tue, 24 Oct 2023 12:30:26 GMT
- Title: Make LLM a Testing Expert: Bringing Human-like Interaction to Mobile GUI
Testing via Functionality-aware Decisions
- Authors: Zhe Liu, Chunyang Chen, Junjie Wang, Mengzhuo Chen, Boyu Wu, Xing Che,
Dandan Wang, Qing Wang
- Abstract summary: 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.
- Score: 23.460051600514806
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Automated Graphical User Interface (GUI) testing plays a crucial role in
ensuring app quality, especially as mobile applications have become an integral
part of our daily lives. Despite the growing popularity of learning-based
techniques in automated GUI testing due to their ability to generate human-like
interactions, they still suffer from several limitations, such as low testing
coverage, inadequate generalization capabilities, and heavy reliance on
training data. Inspired by the success of Large Language Models (LLMs) like
ChatGPT in natural language understanding and question answering, we formulate
the mobile GUI testing problem as a Q&A task. We propose GPTDroid, asking LLM
to chat with the mobile apps by passing the GUI page information to LLM to
elicit testing scripts, and executing them to keep passing the app feedback to
LLM, iterating the whole process. Within this framework, we have also
introduced a functionality-aware memory prompting mechanism that equips the LLM
with the ability to retain testing knowledge of the whole process and conduct
long-term, functionality-based reasoning to guide exploration. We evaluate it
on 93 apps from Google Play and demonstrate that it outperforms the best
baseline by 32% in activity coverage, and detects 31% more bugs at a faster
rate. Moreover, GPTDroid identify 53 new bugs on Google Play, of which 35 have
been confirmed and fixed.
Related papers
- AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? [54.65079443902714]
We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs.
Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
arXiv Detail & Related papers (2024-11-02T13:24:30Z) - MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains [54.117238759317004]
Massive Multitask Agent Understanding (MMAU) benchmark features comprehensive offline tasks that eliminate the need for complex environment setups.
It evaluates models across five domains, including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics.
With a total of 20 meticulously designed tasks encompassing over 3K distinct prompts, MMAU provides a comprehensive framework for evaluating the strengths and limitations of LLM agents.
arXiv Detail & Related papers (2024-07-18T00:58:41Z) - Vision-driven Automated Mobile GUI Testing via Multimodal Large Language Model [27.97964877860671]
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.
arXiv Detail & Related papers (2024-07-03T11:58:09Z) - Large Language Models for Mobile GUI Text Input Generation: An Empirical Study [24.256184336154544]
Large Language Models (LLMs) have shown excellent text-generation capabilities.
This paper extensively investigates the effectiveness of nine state-of-the-art LLMs in Android text-input generation for UI pages.
arXiv Detail & Related papers (2024-04-13T09:56:50Z) - The Earth is Flat? Unveiling Factual Errors in Large Language Models [89.94270049334479]
Large Language Models (LLMs) like ChatGPT are in various applications due to their extensive knowledge from pre-training and fine-tuning.
Despite this, they are prone to generating factual and commonsense errors, raising concerns in critical areas like healthcare, journalism, and education.
We introduce a novel, automatic testing framework, FactChecker, aimed at uncovering factual inaccuracies in LLMs.
arXiv Detail & Related papers (2024-01-01T14:02:27Z) - 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) - Testing the Limits: Unusual Text Inputs Generation for Mobile App Crash
Detection with Large Language Model [23.460051600514806]
This paper proposes InputBlaster to automatically generate unusual text inputs for mobile app crash detection.
It formulates the unusual inputs generation problem as a task of producing a set of test generators, each of which can yield a batch of unusual text inputs.
It is evaluated on 36 text input widgets with cash bugs involving 31 popular Android apps, and results show that it achieves 78% bug detection rate, with 136% higher than the best baseline.
arXiv Detail & Related papers (2023-10-24T09:10:51Z) - MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation [96.71370747681078]
We introduce MLAgentBench, a suite of 13 tasks ranging from improving model performance on CIFAR-10 to recent research problems like BabyLM.
For each task, an agent can perform actions like reading/writing files, executing code, and inspecting outputs.
We benchmark agents based on Claude v1.0, Claude v2.1, Claude v3 Opus, GPT-4, GPT-4-turbo, Gemini-Pro, and Mixtral and find that a Claude v3 Opus agent is the best in terms of success rate.
arXiv Detail & Related papers (2023-10-05T04:06:12Z) - FreshLLMs: Refreshing Large Language Models with Search Engine
Augmentation [92.43001160060376]
We study the factuality of large language models (LLMs) in the context of answering questions that test current world knowledge.
We introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types.
We benchmark a diverse array of both closed and open-source LLMs under a two-mode evaluation procedure that allows us to measure both correctness and hallucination.
Motivated by these results, we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA.
arXiv Detail & Related papers (2023-10-05T00:04:12Z) - 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) - API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs [84.45284695156771]
API-Bank is a groundbreaking benchmark for tool-augmented Large Language Models.
We develop a run evaluation system consisting of 73 API tools.
We construct a comprehensive training set containing 1,888 tool-use dialogues from 2,138 APIs spanning 1,000 distinct domains.
arXiv Detail & Related papers (2023-04-14T14:05:32Z)
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.