Neural Embeddings for Web Testing

Understand

Web test automation techniques often rely on crawlers to infer models of web applications for automated test generation.

• However, current crawlers rely on state equivalence algorithms that struggle to distinguish near-duplicate pages, often leading to redundant test cases and incomplete coverage of application functionality.
• In this paper, we present a model-based test generation approach that employs transformer-based Siamese neural networks (SNNs) to infer web application models more accurately.
• By learning similarity-based representations, SNNs capture structural and textual relationships among web pages, improving near-duplicate detection during crawling and enhancing the quality of inferred models, and thus, the effectiveness of generated test suites.