用大模型高效构建企业网页应用结构，提升自动化测试质量。
An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering
- 基于大模型分层表示网页结构，增强上下文理解能力。
- 在两个真实应用上实现90%和70%的自动测试成功率。
- 适合智能质量工程、自动化测试团队快速落地使用。
本文提出一种利用大语言模型（LLM）表示企业级网页应用结构的新方法，以支持大规模智能质量工程。该方法采用分层表示策略，在保持网页应用复杂关系的同时，优化了大模型的少样本学习能力。整个流程包含五个阶段：全面的DOM分析、多页面整合、测试用例生成、执行与结果分析。针对生成式AI在自动化测试中应用的挑战，本方法通过结构化格式使大模型能够通过上下文学习理解应用架构。我们在两个不同应用上进行了评估：一个电商平台（Swag Labs）和一个医疗应用（MediBox），后者部署于Atalgo工程环境。结果显示，自动测试成功率达到90%和70%，测试用例在多个评价标准下均具有高相关性。研究表明，该表示方法显著提升了大模型生成上下文相关测试用例的能力，整体质量保障效果更好，同时大幅减少测试时间和人力投入。
原文摘要 · Abstract (English)
This paper presents a novel approach to represent enterprise web application structures using Large Language Models (LLMs) to enable intelligent quality engineering at scale. We introduce a hierarchical representation methodology that optimizes the few-shot learning capabilities of LLMs while preserving the complex relationships and interactions within web applications. The approach encompasses five key phases: comprehensive DOM analysis, multi-page synthesis, test suite generation, execution, and result analysis. Our methodology addresses existing challenges around usage of Generative AI techniques in automated software testing by developing a structured format that enables LLMs to understand web application architecture through in-context learning. We evaluated our approach using two distinct web applications: an e-commerce platform (Swag Labs) and a healthcare application (MediBox) which is deployed within Atalgo engineering environment. The results demonstrate success rates of 90\% and 70\%, respectively, in achieving automated testing, with high relevance scores for test cases across multiple evaluation criteria. The findings suggest that our representation approach significantly enhances LLMs' ability to generate contextually relevant test cases and provide better quality assurance overall, while reducing the time and effort required for testing.
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