This research introduces a standardized methodology to evaluate how effectively Large Language Models (LLMs) can generate software test cases, addressing the lack of comprehensive benchmarks in this domain.

• Tackles the challenge of assessing LLM capabilities in automated test case generation
• Provides a systematic approach covering diverse programming scenarios
• Addresses the absence of standardized benchmarks for proper evaluation
• Aims to reduce manual testing effort while maintaining software quality

For engineering teams, this research offers valuable insights into how AI can accelerate the testing process while establishing reliable metrics to evaluate the effectiveness of LLM-generated tests.

A Systematic Approach for Assessing Large Language Models' Test Case Generation Capability