  Code Large Language Models (CodeLLMs) have ushered in a new era in code
generation advancements. However, selecting the best code solutions from all
possible CodeLLM outputs remains a challenge. Previous methods often overlooked
the intricate functional similarities and interactions between solution
clusters. We introduce SRank, a novel reranking strategy for selecting the best
solutions from code generation, focusing on modeling the relationships between
clusters of solutions. By quantifying the functional overlap between solution
clusters, our approach provides a better ranking strategy for code solutions.
Empirical results show that our method achieves remarkable results on the
pass@1 score. For instance, on the Human-Eval benchmark, we achieve 69.66% in
pass@1 with Codex002, 75.31% with WizardCoder, 53.99% with StarCoder, and
60.55% with CodeGen, surpassing state-of-the-art code generation reranking
methods such as CodeT and Coder-Reviewer on the same CodeLLM by a significant
margin (approximately 6.1% improvement on average). Even in scenarios with a
limited number of sampled solutions and test cases, our approach demonstrates
robustness and superiority, marking a new benchmark in code generation
reranking. Our implementation can be found at
https://github.com/FSoft-AI4Code/SRank-CodeRanker.
