ModelRefs / HumanEval Methodology — Methodology

HumanEval Methodology — Methodology

HumanEval evaluates Python code generation from docstrings via hidden unit tests.

Overview

What it measures: Functional correctness of short Python functions generated from natural language specs.

How it works

  • 164 hand-written programming problems.
  • Each problem ships with a function signature, docstring, and hidden unit tests.
  • Models generate a function body; pass@k is computed against tests.
  • pass@1 is the most-reported metric.

Strengths

  • Functional rather than syntactic scoring
  • Easy to reproduce

Limitations

  • Saturated (>95%)
  • Single language
  • Short snippets — no real-world context

Best use cases

Quick code-generation sanity check

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Frequently asked questions

What does HumanEval measure?

Functional correctness of short Python functions generated from natural language specs.

What are its main limitations?

Saturated (>95%) Single language Short snippets — no real-world context

When should I use this benchmark?

Quick code-generation sanity check