IiPCS: Intent-Based In-Context Learning for Project-Specific Code Summarization

Recent years have witnessed growing research interest in automatic source code summarization due to its beneficial potential in software development and maintenance tasks. In the past few years, various deep learning models have been developed to leverage structural and textual features in the code for generating meaningful and succinct summaries. However, the summaries generated by traditional deep learning models often have syntax errors or are meaningless. The emergence of large language models provides an opportunity to overcome the problem. However, the quality of the summaries largely depends on the in-context learning examples of code-summary pairs. In this work, we develop iiPCS, an LLM-based method for code summarization. We retrieve relevant code-summary pairs as in-context learning examples from the same project of the target code, which ensures to generate more project-specific summaries, and use the predicted intent of the target code to pick few-shot examples, which ensures to generate summaries with the correct intent. Experimental results show that iiPCS can generate code summaries with higher quality compared to traditional methods using deep learning and recent methods using LLMs.