Multi-dimensional Constraint-based Test Case Generation and Evaluation Framework for Large Language Models

Addressing the challenges of complex test case design and insufficient coverage in functional testing of large language models, this paper presents a multi-dimensional constraint-based test case generation framework. The framework defines constraint rules across four dimensions: syntactic correctness, semantic consistency, task relevance, and boundary conditions, employing reinforcement learning methods to optimize the test case generation process. Through the design of reward function-based generation strategies, the system can automatically produce high-quality functional test samples covering core tasks including text classification, sentiment analysis, and machine translation. Experimental results demonstrate that test cases generated by this method achieve a 42% improvement in functional coverage compared to random generation methods and a 28% increase in defect detection rate. Further ablation experiments validate the effectiveness of each dimensional constraint, providing a systematic solution for large language model quality assurance.