Applying Natural Language Processing in Automating User Interface Testing

Abstract

This study investigates the application of Natural Language Processing (NLP) in automating user interface (UI) testing for web and mobile applications. The primary research problem addressed is how NLP can effectively interpret and execute test cases from natural language descriptions, aiming to streamline and enhance the testing process. The study employs a design that includes developing an NLP-based framework, generating test cases from natural language inputs, and evaluating the framework's performance and accuracy. Key findings reveal that the NLP framework successfully converts natural language descriptions into actionable test cases with high accuracy. It also performs efficiently in executing these test cases and demonstrates effective error detection and reporting. These results support the hypothesis that NLP can significantly improve UI testing by making test case creation more intuitive and automation more effective. The study concludes that NLP-driven automation offers a valuable advancement in UI testing methodologies, suggesting further exploration of advanced NLP techniques and broader application scenarios.