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Driven by the surge in code generation using large language models (LLMs),numerous benchmarks have emerged to evaluate these LLMs capabilities. Weconducted a large-scale human evaluation of HumanEval and MBPP, two popularbenchmarks for Python code generation, analyzing their diversity anddifficulty. Our findings unveil a critical bias towards a limited set ofprogramming concepts, neglecting most of the other concepts entirely.Furthermore, we uncover a worrying prevalence of easy tasks, potentiallyinflating model performance estimations. To address these limitations, wepropose a novel benchmark, PythonSaga, featuring 185 hand-crafted prompts on abalanced representation of 38 programming concepts across diverse difficultylevels. The robustness of our benchmark is demonstrated by the poor performanceof existing Code-LLMs.

PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs