App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 7

Code recommendation method by fusing semantic and hierarchical structural information

Xu Xiangwei1
Li Yong1,2
Liang Yuwang1
1. College of Computer Science & Technology, Xinjiang Normal University, Ürümqi 830054, China
2. Key Laboratory of Safety-Critical Software of Ministry & Information Technology, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China
DOI: 10.19734/j.issn.1001-3695.2025.10.0462

Abstract

The rapid advancement of software technology has led to the accumulation of large-scale open-source code resources, making code reuse a common practice in software engineering to improve development efficiency and reduce costs. Among these practices, code recommendation technology serves as a key pathway to achieving efficient reuse. However, most existing methods primarily rely on semantic modeling of lexical units, often neglecting cross-function contextual information and the structural information embedded in Abstract Syntax Trees (ASTs) . This oversight results in insufficient semantic understanding and limits further improvement in retrieval accuracy. To address these limitations, this paper proposes SSFCR (Semantic-and Structure-Fused Code Recommendation) , a novel code recommendation method that integrates both semantic and hierarchical structural information. Our approach introduces a dynamic context selection mechanism to adaptively filter relevant contextual information across files and functions. Moreover, we design a hierarchical AST encoder that incorporates a syntax-tag gating mechanism and a layered message aggregation strategy to effectively capture long-range syntactic dependencies in code. Experimental results on multiple public datasets demonstrate that the proposed method significantly outperforms mainstream baseline approaches such as CodeBERT and GraphCodeBERT in key metrics including Recall and Mean Reciprocal Rank (MRR) . Additionally, it exhibits strong cross-lingual and cross-task generalization capabilities.

Foundation Support

新疆维吾尔自治区"天山英才"计划项目(2024TSYCCX0066)
国家自然科学基金资助项目(62241209)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.10.0462
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-25] Accepted Paper

Cite This Article

徐向伟, 李勇, 梁雨望. 融合语义与层次化结构信息的代码推荐方法 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-27). https://doi.org/10.19734/j.issn.1001-3695.2025.10.0462. (Xu Xiangwei, Li Yong, Liang Yuwang. Code recommendation method by fusing semantic and hierarchical structural information [J]. Application Research of Computers, 2026, 43 (7). (2026-03-27). https://doi.org/10.19734/j.issn.1001-3695.2025.10.0462. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)