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Early-Stage Graph Fusion with Refined Graph Neural Networks for Semantic Code Search.

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Název: Early-Stage Graph Fusion with Refined Graph Neural Networks for Semantic Code Search.
Autoři: Ao, Longhao, Qi, Rongzhi
Zdroj: Applied Sciences (2076-3417); Jan2026, Vol. 16 Issue 1, p12, 19p
Témata: GRAPH neural networks, PROGRAMMING language semantics, FLOWGRAPHS, GRAPH connectivity
Abstrakt: Code search has received significant attention in the field of computer science research. Its core objective is to retrieve the most semantically relevant code snippets by aligning the semantics of natural language queries with those of programming languages, thereby contributing to improvements in software development quality and efficiency. As the scale of public code repositories continues to expand rapidly, the ability to accurately understand and efficiently match relevant code has become a critical challenge. Furthermore, while numerous studies have demonstrated the efficacy of deep learning in code-related tasks, the mapping and semantic correlations are often inadequately addressed, leading to the disruption of structural integrity and insufficient representational capacity during semantic matching. To overcome these limitations, we propose the Functional Program Graph for Code Search (called FPGraphCS), a novel code search method that leverages the construction of functional program graphs and an early fusion strategy. By incorporating abstract syntax tree (AST), data dependency graph (DDG), and control flow graph (CFG), the method constructs a comprehensive multigraph representation, enriched with contextual information. Additionally, we propose an improved metapath aggregation graph neural network (IMAGNN) model for the extraction of code features with complex semantic correlations from heterogeneous graphs. Through the use of metapath-associated subgraphs and dynamic metapath selection via a graph attention mechanism, FPGraphCS significantly enhances its search capability. The experimental results demonstrate that FPGraphCS outperforms existing baseline methods, achieving an MRR of 0.65 and ACC@10 of 0.842, showing a significant improvement over previous approaches. [ABSTRACT FROM AUTHOR]
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  Data: Early-Stage Graph Fusion with Refined Graph Neural Networks for Semantic Code Search.
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  Data: <searchLink fieldCode="AR" term="%22Ao%2C+Longhao%22">Ao, Longhao</searchLink><br /><searchLink fieldCode="AR" term="%22Qi%2C+Rongzhi%22">Qi, Rongzhi</searchLink>
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  Data: Applied Sciences (2076-3417); Jan2026, Vol. 16 Issue 1, p12, 19p
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  Data: <searchLink fieldCode="DE" term="%22GRAPH+neural+networks%22">GRAPH neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22PROGRAMMING+language+semantics%22">PROGRAMMING language semantics</searchLink><br /><searchLink fieldCode="DE" term="%22FLOWGRAPHS%22">FLOWGRAPHS</searchLink><br /><searchLink fieldCode="DE" term="%22GRAPH+connectivity%22">GRAPH connectivity</searchLink>
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  Data: Code search has received significant attention in the field of computer science research. Its core objective is to retrieve the most semantically relevant code snippets by aligning the semantics of natural language queries with those of programming languages, thereby contributing to improvements in software development quality and efficiency. As the scale of public code repositories continues to expand rapidly, the ability to accurately understand and efficiently match relevant code has become a critical challenge. Furthermore, while numerous studies have demonstrated the efficacy of deep learning in code-related tasks, the mapping and semantic correlations are often inadequately addressed, leading to the disruption of structural integrity and insufficient representational capacity during semantic matching. To overcome these limitations, we propose the Functional Program Graph for Code Search (called FPGraphCS), a novel code search method that leverages the construction of functional program graphs and an early fusion strategy. By incorporating abstract syntax tree (AST), data dependency graph (DDG), and control flow graph (CFG), the method constructs a comprehensive multigraph representation, enriched with contextual information. Additionally, we propose an improved metapath aggregation graph neural network (IMAGNN) model for the extraction of code features with complex semantic correlations from heterogeneous graphs. Through the use of metapath-associated subgraphs and dynamic metapath selection via a graph attention mechanism, FPGraphCS significantly enhances its search capability. The experimental results demonstrate that FPGraphCS outperforms existing baseline methods, achieving an MRR of 0.65 and ACC@10 of 0.842, showing a significant improvement over previous approaches. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/app16010012
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      – Code: eng
        Text: English
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        PageCount: 19
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      – SubjectFull: GRAPH neural networks
        Type: general
      – SubjectFull: PROGRAMMING language semantics
        Type: general
      – SubjectFull: FLOWGRAPHS
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      – SubjectFull: GRAPH connectivity
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      – TitleFull: Early-Stage Graph Fusion with Refined Graph Neural Networks for Semantic Code Search.
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              Text: Jan2026
              Type: published
              Y: 2026
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