Large Language Models for Automated Web-Form-Test Generation: An Empirica

This report presents a framework that enables the reproduction and extension of our empirical evaluations using Large Language Models (LLMs) for automated web-form-test generation. The framework includes HTML pruning , context construction , prompt design , LLM communication , and web-form-test insertion . It involves the construction of three types of prompts (from HTML) to guide the test generation: Raw HTML for Task Prompt (RH-P); LLM-Processed HTML for Task Prompt (LH-P); and Parser-Processed HTML for Task Prompt (PH-P). The framework provides an LLM communication module that standardizes interactions with provider Application Programming Interfaces (APIs). Our study utilized public-API models and demonstrated that PH-P consistently achieved a higher successfully-submitted rate (SSR) than RH-P and LH-P. To support the replication of our work, we have released the source code, a dataset subset, and the relevant scripts.