GUI-Based Software Testing: An Automated Approach Using GPT-4 and Selenium WebDriver
Daniel Zimmermann, Anne Koziolek
Abstract
This paper presents a novel method for GUI testing in web applications that largely automates the process by integrating the advanced language model GPT-4 with Selenium, a popular web application testing framework. Unlike traditional deep learning approaches, which require extensive training data, GPT-4 is pre-trained on a large corpus, giving it significant generalisation and inference capabilities. These capabilities allow testing without the need for recorded data from human testers, significantly reducing the time and effort required for the testing process. We also compare the efficiency of our integrated GPT-4 approach with monkey testing, a widely used technique for automated GUI testing where user input is randomly generated. To evaluate our approach, we implemented a web calculator with an integrated code coverage system. The results show that our integrated GPT-4 approach provides significantly better branch coverage compared to monkey testing. These results highlight the significant potential of integrating specific AI models such as GPT-4 and automated testing tools to improve the accuracy and efficiency of GUI testing in web applications.