MineValiCoder: Reliable Code Generation with Test Case Quality Mining and Bipartite Graph-Based Mutual Validation

LLM-based test-driven code generation struggles when only natural-language requirements exist, since automatically generated tests can be faulty or inconsistent, misleading optimization. MineValiCoder tackles this with a closed-loop framework: filtering unreliable tests via self-validation, iteratively refining diverse code candidates, and using a bipartite graph model to jointly validate code-test interactions for stable final selection. Tested across four LLMs and major benchmarks, it achieved strong Pass@1 scores including 96.34% on HumanEval. Applications include automated software engineering pipelines, reducing reliance on human-crafted tests, and improving reliability of AI code generation tools in production development workflows. Authors: Zhen Zhao, Qihang Yang, Feifei Dai, Xiangfang Li, Bo Li Paper: https://arxiv.org/abs/2607.22471v1