Pre-trained large language models (LLMs) have emerged as a breakthrough technology in code intelligence such as code generation. Recently, many works have found that LLMs can generate a correct code solution when it is allowed to make numerous attempts. Consequently, a recent trend is to do a large-scale sampling of codes from LLMs and then rank the code to select the most suitable code (A process called code ranking). A common code ranking approach ranks the code by running it against a set of LLM-generated test cases in the form of assert statements. However, existing approaches overlook how humans design test cases through systematic behaviors, which are essential for creating reliable tests for code ranking. Moreover, humans often use input–output examples to clarify and articulate the intended functionality, rather than merely to rank code. To address these gaps, we propose RankAgent, a human-inspired agent-based framework that systematically simulates human test design behaviors. Extensive experiments on five LLMs (including both open- and closed-source models) and two benchmarks (HumanEval+ and LiveCodeBench) show that RankAgent achieves notable and consistent improvements.