Explosion of Papers on Benchmarking LLMs for Code Generation

A flurry of recent arXiv preprints focuses on benchmarking large language models (LLMs) for code generation. These papers cover various aspects, including LLMs solving real-world GitHub issues, self-invoking code generation, API usage, stability analysis, and evaluations across the entire software development lifecycle. Researchers have developed diverse benchmarks like SWE-bench, HumanEval Pro, SEAL, and DevEval, along with corresponding metrics, aiming for a more comprehensive evaluation of LLM code generation capabilities and driving progress in the field.