Work

cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree

Year: 2025

Type: conference-paper

Abstract: Retrieval-Augmented Generation (RAG) has become essential for large-scale code generation, grounding predictions in external code corpora to improve factuality.However, a critical yet underexplored aspect of RAG pipelines is chunking-the process of dividing documents into retrievable units.Existing linebased chunking heuristics often break semantic structures, splitting functions or merging unrelated code, which can degrade generation quality.We propose chunking via Abstract Syntax Trees (CAST), a structure-aware method that recursively breaks large AST nodes into smaller chunks and merges sibling nodes while respecting size limits.This approach generates selfcontained, semantically coherent units across programming languages and tasks, improving performance on diverse code generation tasks, e.g., boosting Recall@5 by 4.3 points on Re-poEval retrieval and Pass@1 by 2.67 points on SWE-bench generation.Our work highlights the importance of structure-aware chunking for scaling retrieval-enhanced code intelligence.

Source: Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP)

DOI: 10.18653/v1/2025.findings-emnlp.430

Authors: Yilin Zhang, Xinran Zhao, Zora Zhiruo Wang, Chenyang Yang, Jiayi Wei, Tongshuang Wu

Institution: Carnegie Mellon University

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Office of Naval Research

OpenAlex ID: W4416034875