Beyond Synthetic Benchmarks: Evaluating LLM Performance on Real-World Class-Level Code Generation

A new study has shed light on the performance of large language models (LLMs) in generating class-level code for real-world software projects. While LLMs have shown promise in function-level code generation, their effectiveness in creating accurate class-level implementations has been less understood. This research introduces a unique benchmark based on open-source repositories, allowing for a more practical evaluation of LLMs' generalization capabilities. This is significant as it helps developers and researchers understand the limitations and strengths of LLMs in real-world applications, paving the way for improved tools and methodologies in software development.