Project-specific code summarization with in-context learning

Automatically generating summaries for source code has emerged as a valuable task in software development. While state-of-the-art (SOTA) approaches have demonstrated significant efficacy in summarizing general code, they seldom concern code summarization for a specific project. Project-specific code summarization (PCS) poses special challenges due to the scarce availability of training data and the unique styles of different projects. In this paper, we empirically analyze the performance of Large Language Models (LLMs) on PCS tasks. Our study reveals that using appropriate prompts is an effective way to solicit LLMs for generating project-specific code summaries. Based on these findings, we propose a novel project-specific code summarization approach called P-CodeSum. P-CodeSum gathers a repository-level pool of (code, summary) examples to characterize the project-specific features. Then, it trains a neural prompt selector on a high-quality dataset crafted by LLMs using the example pool. The prompt selector offers relevant and high-quality prompts for LLMs to generate project-specific summaries. We evaluate against a variety of baseline approaches on six PCS datasets. Experimental results show that the P-CodeSum improves the performance by 5.9% (RLPG) to 101.51% (CodeBERT) on BLEU-4 compared to the state-of-the-art approaches in project-specific code summarization.