关键词: 大语言模型, 单元测试用例生成, 路径引导, 迭代优化, 控制流图分析

Abstract: Large language model (LLM) has shown considerable potential in the automatic generation of unit test cases, yet limitations remain in covering complex structures and critical paths, resulting in gaps in coverage and accuracy. To address this issue, a path guidance iterative optimization strategy was proposed, integrating control flow graph analysis with a C-P-R (Context-Prompt-Response) template to enhance path guidance during test generation. In addition, a dual-verification mechanism was introduced to improve the reliability of generated cases, while iterative refinement was employed to progressively fill coverage blind spots. Experiments were conducted across multiple models and task scenarios, with systematic evaluations on key metrics including statement coverage, branch coverage, and assertion accuracy. Experimental results demonstrated that the proposed strategy achieved an average of 97% statement coverage and 94% branch coverage, while maintaining strong adaptability and stability across different models. These findings indicate that the combination of path guidance and iterative optimization significantly enhances the coverage and reliability of automatically generated test cases, providing a feasible new approach for automated testing and verification of complex software systems.

Key words: Large Language Model (LLM), unit test case generation, path guidance, iterative optimization, control flow graph analysis