zsLLMCode introduces a novel approach to generate high-quality code embeddings using large language models without requiring costly supervised training or fine-tuning.

• Leverages zero-shot learning to adapt LLMs for code embedding tasks
• Demonstrates superior performance on code-clone detection and code clustering tasks
• Achieves state-of-the-art results while significantly reducing computational resources
• Offers a more sustainable approach to code analysis in software engineering

This research matters for Engineering because it makes advanced code analysis more accessible and efficient, enabling better code management, bug detection, and software maintenance without extensive training infrastructure.

Original Paper: zsLLMCode: An Effective Approach for Code Embedding via LLM with Zero-Shot Learning