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Mengzhen Wang

2 accepted papers

2026

SRACG: A Code Generation Framework with Selective Retrieval Augmentation

AAAI 2026technical

Large Language Models (LLMs) have demonstrated remarkable performance in code generation, offering new possibilities for translating natural language into executable programs. To further enhance LLMs’ code generation capabilities, Retrieval-Augmented Generation (RAG) has emerged as a promising strat

Cited by 0SourcePDFScholar
2025

Fine-Grained Features-based Code Search for Precise Query-Code Matching

COLING 2025main

Code search aims to quickly locate target code snippets from databases using natural language queries, which promotes code reusability. Existing methods can effectively obtain aligned token-level and query word-level features. However, these studies usually represent the semantics of code and query…

Cited by 1SourcePDFScholar