  We propose a novel method for code summarization utilizing Heterogeneous Code
Representations (HCRs) and our specially designed HierarchyNet. HCRs
effectively capture essential code features at lexical, syntactic, and semantic
levels by abstracting coarse-grained code elements and incorporating
fine-grained program elements in a hierarchical structure. Our HierarchyNet
method processes each layer of the HCR separately through a unique combination
of the Heterogeneous Graph Transformer, a Tree-based CNN, and a Transformer
Encoder. This approach preserves dependencies between code elements and
captures relations through a novel Hierarchical-Aware Cross Attention layer.
Our method surpasses current state-of-the-art techniques, such as PA-Former,
CAST, and NeuralCodeSum.
