LLM-Based Method Name Suggestion with Automatically Generated Context-Rich Prompts

Accurate method naming is crucial for code readability and maintainability. However, manually creating concise and meaningful names remains a significant challenge. To this end, in this paper, we propose an approach based on Large Language Model (LLMs) to suggest method names according to function descriptions. The key of the approach is ContextCraft , an automated algorithm for generating context-rich prompts for LLM that suggests the expected method names according to the prompts. For a given query (functional description), it retrieves a few best examples whose functional descriptions have the greatest similarity with the query. From the examples, it identifies tokens that are likely to appear in the final method name as well as their likely positions, picks up pivot words that are semantically related to tokens in the according method names, and specifies the evaluation results of the LLM on the selected examples. All such outputs (tokens with probabilities and position information, pivot words accompanied by associated name tokens and similarity scores, and evaluation results) together with the query and the selected examples are then filled in a predefined prompt template, resulting in a context-rich prompt. This context-rich prompt reduces the randomness of LLMs by focusing the LLM’s attention on relevant contexts, constraining the solution space, and anchoring results to meaningful semantic relationships. Consequently, the LLM leverages this prompt to generate the expected method name, producing a more accurate and relevant suggestion. We evaluated the proposed approach with 43k real-world Java and Python methods accompanied by functional descriptions. Our evaluation results suggested that it significantly outperforms the state-of-the-art approach RNN-att-Copy , improving the chance of exact match by 52% and decreasing the edit distance between generated and expected method names by 32%. Our evaluation results also suggested that the proposed approach worked well for various LLMs, including ChatGPT-3.5, ChatGPT-4, ChatGPT-4o, Gemini-1.5, and Llama-3.