VISCA: Inferring Component Abstractions for Automated End-to-End Testing

Providing optimal contextual input presents a significant challenge for automated end-to-end (E2E) test generation using large language models (LLMs), a limitation that current approaches inadequately address. This paper introduces Visual-Semantic Component Abstractor (VISCA), a novel method that transforms webpages into a hierarchical, semantically rich component abstraction. VISCA starts by partitioning webpages into candidate segments utilizing a novel heuristic-based segmentation method. These candidate segments subsequently undergo classification and contextual information extraction via multimodal LLM-driven analysis, facilitating their abstraction into a predefined vocabulary of user interface (UI) components. This component-centric abstraction offers a more effective contextual basis than prior approaches, enabling more accurate feature inference and robust E2E test case generation. Our evaluations demonstrate that the test cases generated by VISCA achieve an average feature coverage of 92%, exceeding the performance of the state-of-the-art LLM-based E2E test generation method by 16%.