English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

(Source) code summarization is the task of automatically generating naturallanguage summaries for given code snippets. Such summaries play a key role inhelping developers understand and maintain source code. Recently, with thesuccessful application of large language models (LLMs) in numerous fields,software engineering researchers have also attempted to adapt LLMs to solvecode summarization tasks. The main adaptation schemes include instructionprompting and task-oriented fine-tuning. However, instruction promptinginvolves designing crafted prompts for zero-shot learning or selectingappropriate samples for few-shot learning and requires users to haveprofessional domain knowledge, while task-oriented fine-tuning requires hightraining costs. In this paper, we propose a novel prompt learning framework forcode summarization called PromptCS. PromptCS trains a prompt agent that cangenerate continuous prompts to unleash the potential for LLMs in codesummarization. Compared to the human-written discrete prompt, the continuousprompts are produced under the guidance of LLMs and are therefore easier tounderstand by LLMs. PromptCS freezes the parameters of LLMs when training theprompt agent, which can greatly reduce the requirements for training resources.We evaluate PromptCS on the CodeSearchNet dataset involving multipleprogramming languages. The results show that PromptCS significantly outperformsinstruction prompting schemes on all four widely used metrics. In some baseLLMs, e.g., CodeGen-Multi-2B and StarCoderBase-1B and -3B, PromptCS evenoutperforms the task-oriented fine-tuning scheme. More importantly, thetraining efficiency of PromptCS is faster than the task-oriented fine-tuningscheme, with a more pronounced advantage on larger LLMs. The results of thehuman evaluation demonstrate that PromptCS can generate more good summariescompared to baselines.

A Prompt Learning Framework for Source Code Summarization