Cypress Copilot: Development of an AI Assistant for Boosting Productivity and Transforming Web Application Testing

In today’s fast-paced software development environment, Agile methodologies demand rapid delivery and continuous improvement, making automated testing essential for maintaining quality and accelerating feedback loops. Our study addresses the challenges of developing and maintaining automation code for web-based application testing. In this paper, we propose a novel approach that leverages large language models (LLMs) and a novel prompt technique, few-shot chain, to automate code generation for web application testing. We chose the Behavior-Driven Development (BDD) methodology owing to its advantages and selected the Cypress tool for automating web application testing, as it is one of the most popular and rapidly growing frameworks in this domain. We comprehensively evaluated various OpenAI models, including GPT-4-Turbo, GPT-4o, and GitHub Copilot, using zero-shot and few-shot chain prompt techniques. Furthermore, we extensively validated with a vast set of test cases to identify the optimal approach. Our results indicate that the Cypress automation code generated by GPT-4o using a few-shot chained prompt approach excels in generating complete code for each test case, with fewer empty methods and improved syntactical accuracy and maintainability. Based on these findings, we developed a novel open-source Visual Studio Code (IDE) extension, “Cypress Copilot” utilizing GPT-4o and a few-shot chain prompt technique, which has shown promising results. Finally, we validate the Cypress Copilot tool by generating automation code for end-to-end web tests, demonstrating its effectiveness in testing various web applications and its ability to streamline development processes. More importantly, we are releasing this tool to the open-source community, as it has the potential to be a promising partner in enhancing productivity in web application automation testing.