Code Generation and Repository-Level Software Engineering Benchmarks — A Field Guide to LLM Benchmarks | by Adnan Masood, PhD. | Medium

If you’re trying to evaluate coding models for production use, the uncomfortable truth is that “coding ability” is not a single skill. It’s a bundle of capabilities that include: translating intent into changes, reading unfamiliar code, making correct edits with minimal collateral damage, validating fixes via tests, and communicating tradeoffs. Benchmarks exist because we need shared yardsticks — but the ones that dominate today measure different slices of the workflow, and they can be misleading if you treat them as a single leaderboard race. At one end of the spectrum, HumanEval and MBPP evaluate unit-test-based synthesis: given a prompt, produce code that passes hidden tests. This is a clean, automatable setup and a useful baseline for “can the model write correct functions?” The operational challenge is that these benchmarks often collapse engineering into completion: they don’t require repo navigation, dependency management, integration, refactoring, or test authoring. And their c

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• Al-Ekram Elahee Hridoy