Rethinking Verification for LLM Code Generation: From Generation to
  Testing
Authors: name: Zihan Ma, name: Taolin Zhang, name: Maosong Cao, name: Wenwei Zhang, name: Minnan Luo, name: Songyang Zhang, name: Kai Chen

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.