An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering
This addresses the challenge of intelligent quality engineering at scale for software developers and testers, though it appears incremental as it builds on existing Generative AI techniques.
The paper tackles the problem of automated software testing for enterprise web applications by developing a hierarchical representation methodology using Large Language Models (LLMs), achieving success rates of 90% and 70% in automated testing for two distinct applications.
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.