Work

Using Large Language Model to Fill in Web Forms to Support Automated Web Application Testing

Year: 2025

Type: article

Abstract: Web applications, widely used by enterprises for business services, require extensive testing to ensure functionality. Performing form testing with random input data often takes a long time to complete. Previously, we introduced a model for automated testing of web applications using reinforcement learning. The model was trained to fill form fields with fixed input values and click buttons. However, the performance of this model was limited by a fixed set of input data and the imprecise detection of successful form submission. This paper proposes a model to address these limitations. First, we use a large language model with data fakers to generate a wide variety of input data. Additionally, whether form submission is successful is partially determined by GPT-4o. Experiments show that our method increases average statement coverage by 2.3% over the previous model and 7.7% to 11.9% compared to QExplore, highlighting its effectiveness.

Source: Information

DOI: 10.3390/info16020102

Authors: Feng-Kai Chen, Chien-Hung Liu, Shingchern D. You

Institution: National Taipei University of Technology

Language: English

Topic: Software Testing and Debugging Techniques

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: National Science and Technology Council