Year: 2025
Type: article
Abstract: In today’s rapid software development landscape, ensuring software quality through comprehensive testing is both crucial and increasingly challenging. Traditional test case generation methods are time-consuming, error-prone, and often fail to adapt to evolving application requirements. This project introduces an AI-powered automated test case generation system that leverages OpenAI’s GPT language model integrated into a Streamlit-based interface to revolutionize the way test cases are created. By accepting inputs such as functional requirements, code snippets, or user stories, the system dynamically generates diverse and context-aware test cases—covering edge, boundary, and negative scenarios. Real-time output, confidence scoring, and an intuitive GUI enhance usability for both developers and QA professionals. With support for various programming languages and document formats (e.g., PDF, DOCX), the platform ensures high coverage and faster feedback loops, making it ideal for agile and CI/CD environments. The combination of prompt engineering, AI inference, and real-time interactivity marks a significant advancement in intelligent software testing tools. This project reflects the increasing demand for smart, scalable, and human-like AI systems that can understand software logic and testing principles effectively.
Authors: Sreelatha Bijili, Nandana Ande, Vinesh Doddi, Sai Aryan Meesala, Thilak Chinta, Nithin Soma
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410242538