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Coding benchmarks

Understand code generation, repository repair and software engineering evaluations before comparing model results.

Coding benchmarks test whether a model can produce or change software that meets a specified requirement. The unit of work matters. Completing a small function is different from finding the right files in an unfamiliar repository, editing them and passing a regression suite. HumanEval illustrates function completion; SWE-bench illustrates issue-driven repository changes. Choose the task that resembles the work you need done, then inspect how the result was verified.

What these benchmarks test

  • Function completion measures whether generated code satisfies executable examples and hidden tests.
  • Repository tasks also exercise code navigation, dependency handling and interaction with a development environment.
  • Language, repository and task-type coverage determine which engineering situations a result actually describes.

How to read the results

  • Match the exact dataset version and split. A verified subset and a full collection are separate comparisons.
  • Record the agent framework, available tools, retries and time or token budget alongside the model.
  • Check whether the metric counts a successful first attempt, any successful attempt, or tasks resolved by a submitted patch.

What to watch for

  • Passing tests does not establish maintainability, secure design or correctness outside the tested behavior.
  • A familiar public task may be less informative about new engineering work; inspect contamination controls and task construction.

References

  1. Evaluating Large Language Models Trained on Code
  2. SWE-bench official benchmark and leaderboards

Benchmarks in this topic 199 source records

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