This research introduces a novel approach that fine-tunes GPT-3.5 to automatically generate test cases from natural language requirements, directly supporting Test-Driven Development workflows.

• Custom LLM fine-tuning on curated test case datasets enables high-quality test generation
• Requirements-to-test transformation eliminates the need for existing code, supporting true TDD practices
• Engineering productivity gains through automated conversion of business requirements to executable test cases
• Reduced development time while maintaining testing quality and coverage

This innovation matters for Engineering teams by bridging the gap between business requirements and technical implementation, enabling faster iteration cycles while maintaining software quality standards.

Enhancing Large Language Models for Text-to-Testcase Generation