Learning to Generate Structured Code Summaries From Hybrid Code Context

Abstract

Code summarization aims to automatically generate natural language descriptions for code, and has become a rapidly expanding research area in the past decades. Unfortunately, existing approaches mainly focus on the “one-to-one” mapping from methods to short descriptions, which hinders them from becoming practical tools: 1) The program context is ignored, so they have difficulty in predicting keywords outside the target method; 2) They are typically trained to generate brief function descriptions with only one sentence in length, and therefore have difficulty in providing specific information. These drawbacks are partially due to the limitations of public code summarization datasets. In this paper, we first build a large code summarization dataset including different code contexts and summary content annotations, and then propose a deep learning framework that learns to generate structured code summaries from hybrid program context, named StructCodeSum. It provides both an LLM-based approach and a lightweight approach which are suitable for different scenarios. Given a target method, StructCodeSum predicts its function description, return description, parameter description, and usage description through hybrid code context, and ultimately builds a Javadoc-style code summary. The hybrid code context consists of path context, class context, documentation context and call context of the target method. Extensive experimental results demonstrate: 1) The hybrid context covers more than 70% of the summary tokens in average and significantly boosts the model performance; 2) When generating function descriptions, StructCodeSum outperforms the state-of-the-art approaches by a large margin; 3) According to human evaluation, the quality of the structured summaries generated by our approach is better than the documentation generated by Code Llama.