An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering

TL;DR

This paper introduces a hierarchical representation method for enterprise web applications using Large Language Models, improving automated testing efficiency and quality assurance through structured understanding of complex web architectures.

Contribution

The paper presents a novel hierarchical representation approach that leverages LLMs for understanding web application structures, enabling more effective automated testing at scale.

Findings

Achieved 90% success rate in automated testing for e-commerce application

Attained 70% success rate in healthcare application testing

Enhanced LLMs' ability to generate relevant test cases with reduced effort

Abstract

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.