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

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.

Keywords

• large language model
• prompt tuning
• web app testing
• web crawler
• GPT-4o