AI-Powered Natural Language Processing for Automated Test Case Generation: A Transformer-Based Method for Web Application Testing

Author(s)	Srikanth Kavuri
Country	United States
Abstract	Software testing plays an important role in the software development lifecycle by ensuring application reliability and functionality. However, traditional manual test case generation is often time-consuming, resource-intensive, and susceptible to human errors. This study proposes an AI-driven approach for automating test case generation and execution in web applications using Natural Language Processing (NLP) and automation frameworks. We leverage the Text-to-Text Transfer Transformer (T5) model to convert natural language user stories into structured test cases, which are then executed using the Selenium WebDriver framework. By eliminating the need for manually written test cases, our method enhances efficiency and scalability in software testing. The effectiveness of the proposed approach is evaluated based on the quality of generated test cases, execution accuracy, and applicability across various domains. The results indicate that AI-powered test case generation significantly reduces testing efforts while maintaining high accuracy. This research underscores the transformative potential of NLP-based automation in modernizing software testing methodologies.
Field	Engineering
Published In	Volume 4, Issue 10, October 2023
Published On	2023-10-07
DOI	https://doi.org/10.70528/IJLRP.v4.i10.1837
Short DOI	https://doi.org/hbcdc2

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