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arXiv 提交日期: 2026-04-29
📄 Abstract - Graph Construction and Matching for Imperative Programs using Neural and Structural Methods

Reusing verification artefacts requires identifying structural and semantic similarities across programs and their specifications. In this paper, we focus on graph construction as a foundational step toward this goal. We present a pipeline that converts imperative programs and their annotations into typed, attributed graphs. Our experiments cover datasets including C with ACSL, Java with JML, and Dafny for C\#. The pipeline integrates abstract syntax tree parsing with semantic embeddings derived from models such as SentenceTransformer and CodeBERT. This enables the generation of graph representations that capture both structural relationships and semantic context. Our results show that consistent graph representations can be constructed across different languages and annotation styles. This work provides a practical basis for future steps in semantic enrichment and approximate graph matching for scalable verification artefact reuse.

顶级标签: machine learning systems
详细标签: graph construction code representation semantic embeddings program verification cross-language 或 搜索:

面向命令式程序的基于神经与结构方法的图构建与匹配 / Graph Construction and Matching for Imperative Programs using Neural and Structural Methods


1️⃣ 一句话总结

本文提出了一种将不同编程语言(如C、Java、Dafny)的代码及其注释自动转化为统一结构图的方法,结合语法解析和AI语言模型的语义理解,为未来大规模重用程序验证成果提供了基础。

源自 arXiv: 2604.26578