Rethinking Verification for LLM Code Generation: From Generation to Testing

Large language models (LLMs) have recently achieved notable success in code‑generation benchmarks such as HumanEval and LiveCodeBench. However, a detailed examination reveals that these evaluation suites often comprise only a limited number of homogeneous test cases, resulting in subtle faults going undetected. This not only artificially inflates measured performance but also compromises accurate reward estimation in reinforcement learning frameworks utilizing verifiable rewards (RLVR). To address these critical shortcomings, we systematically investigate the test-case generation (TCG) task by proposing multi-dimensional metrics designed to rigorously quantify test-suite thoroughness. Furthermore, we introduce a human-LLM collaborative method (SAGA), leveraging human programming expertise with LLM reasoning capability, aimed at significantly enhancing both the coverage and the quality of generated test cases. In addition, we develop a TCGBench to facilitate the study of the TCG task. Experiments show that SAGA achieves a detection rate of 90. 62\% and a verifier accuracy of 32. 58\% on TCGBench. The Verifier Accuracy (Verifier Acc) of the code generation evaluation benchmark synthesized by SAGA is 10. 78\% higher than that of LiveCodeBench-v6. These results demonstrate the effectiveness of our proposed method. We hope this work contributes to building a scalable foundation for reliable LLM code evaluation, further advancing RLVR in code generation, and paving the way for automated adversarial test synthesis and adaptive benchmark integration.

Authors

• Zihan Ma School of Computer Science and Technology, Xi’an Jiaotong University, Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi’an Jiaotong University. Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, Xi’an Jiaotong University.
• Taolin Zhang Alibaba Group, Hangzhou, China
• Junnan Liu
• Wenwei Zhang Nanyang Technological University
• Minnan Luo School of Computer Science and Technology, Xi’an Jiaotong University, Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi’an Jiaotong University. Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, Xi’an Jiaotong University.
• Songyang Zhang University of Louisiana at Lafeyette
• Kai Chen Shanghai Key Lab of Intell. Info. Processing, School of Computer Science, Fudan University, Shanghai Collaborative Innovation Center on Intelligent Visual Computing

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