(Source) code summarization is the task of automatically generating natural
language summaries for given code snippets. Such summaries play a key role in
helping developers understand and maintain source code. Recently, with the
successful application of large language models (LLMs) in numerous fields,
software engineering researchers have also attempted to adapt LLMs to solve
code summarization tasks. The main adaptation schemes include instruction
prompting and task-oriented fine-tuning. However, instruction prompting
involves designing crafted prompts for zero-shot learning or selecting
appropriate samples for few-shot learning and requires users to have
professional domain knowledge, while task-oriented fine-tuning requires high
training costs. In this paper, we propose a novel prompt learning framework for
code summarization called PromptCS. PromptCS trains a prompt agent that can
generate continuous prompts to unleash the potential for LLMs in code
summarization. Compared to the human-written discrete prompt, the continuous
prompts are produced under the guidance of LLMs and are therefore easier to
understand by LLMs. PromptCS freezes the parameters of LLMs when training the
prompt agent, which can greatly reduce the requirements for training resources.
We evaluate PromptCS on the CodeSearchNet dataset involving multiple
programming languages. The results show that PromptCS significantly outperforms
instruction prompting schemes on all four widely used metrics. In some base
LLMs, e.g., CodeGen-Multi-2B and StarCoderBase-1B and -3B, PromptCS even
outperforms the task-oriented fine-tuning scheme. More importantly, the
training efficiency of PromptCS is faster than the task-oriented fine-tuning
scheme, with a more pronounced advantage on larger LLMs. The results of the
human evaluation demonstrate that PromptCS can generate more good summaries
compared to baselines.

本文提出了一种用于代码摘要的新型提示学习框架，称为 PromptCS，该框架训练了一个能够生成连续提示的提示代理，以释放大语言模型在代码摘要中的潜力，相比于人工编写的离散提示，连续提示更易于大语言模型理解。PromptCS 在 CodeSearchNet 数据集上的评估结果表明，优于所有四个广泛使用的度量标准的指导学习方案，并且在某些基础大语言模型上，例如 CodeGen-Multi-2B 和 StarCoderBase-1B 和 - 3B，甚至优于任务导向的微调方案。重要的是，PromptCS 的训练效率比任务导向的微调方案更快，在较大的大语言模型上具有更明显的优势。人工评估的结果表明，与基准相比，PromptCS 能够生成更好的摘要。