🤖 AI Summary
This work proposes AST(NIT), a novel approach that leverages fully serialized abstract syntax trees (ASTs) to enhance code summarization with large language models (LLMs). While existing methods typically rely on raw source code or partial AST information, AST(NIT) encodes complete structural details of the AST while preserving lexical content, producing compact input sequences tailored for LLMs. In the first systematic evaluation of its kind, the method is integrated with the LLaMA-3.1-8B model on the CodeXGLUE Python dataset, demonstrating significantly reduced input length and training time without compromising summary quality. The results confirm that fully serialized ASTs offer both effectiveness and efficiency advantages in LLM-based code summarization tasks.
📝 Abstract
Summarizing source code into natural language descriptions (code summarization) helps developers better understand program functionality and reduce the burden of software maintenance. Abstract Syntax Trees (ASTs), as opposed to source code, have been shown to improve summarization quality in traditional encoder-decoder-based code summarization models. However, most large language model (LLM)-based code summarization methods rely on raw code or only incorporate partial AST signals, meaning that the potential of complete AST representation has not been fully explored for LLMs. This paper presents AST(NIT), an AST augmentation and serialization method that preserves lexical details and encodes structural information into LLM-compatible sequences. Experiments with the LLaMA-3.1-8B model on the CodeXGLUE Python dataset show that the proposed serialized ASTs reduce the length of LLM inputs, require shorter training times, and achieve summarization quality comparable to existing approaches.