evaluating-code-models

Benchmarking code generation models with pass@k scoring requires running standardized evaluation suites like HumanEval or MBPP using configurable sampling and generation limits to measure correctness and robustness.

Evaluating code models across multiple programming languages is supported through MultiPL-E variants covering 18 languages, applying benchmarks to instruction-tuned and fill-in-the-middle scenarios for comprehensive multi-language coverage.

Docker-based isolation is required when executing code generation benchmarks if your environment demands secure task execution, ensuring safe evaluation during pass@k scoring and custom benchmark testing.

Building custom benchmark tasks for code generation evaluation involves registering new tasks with unit-test or text-metric scoring, then analyzing generation outputs for leaderboard-style reporting to compare model performance.

Comparing code models on HumanEval and MBPP is best achieved by running the same benchmark suite across multiple models with configurable sampling to identify which performs best on correctness and language coverage.