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Motivated by the increasing popularity of code generation from humandescriptions using large language models (LLMs), several benchmarks have beenproposed to assess the capabilities of existing and emerging models. This studypresents a large-scale human evaluation of HumanEval and MBPP, two widely usedbenchmarks for Python code generation, focusing on their diversity anddifficulty. Our findings reveal a significant bias towards a limited number ofprogramming concepts, with negligible or no representation of most concepts.Additionally, we identify a concerningly high proportion of easy programmingquestions, potentially leading to an overestimation of model performance oncode generation tasks.

Boldly Going Where No Benchmark Has Gone Before: Exposing Bias and Shortcomings in Code Generation Evaluation