Graph Construction and Matching for Imperative Programs using Neural and Structural Methods
For researchers in program verification, this provides a practical foundation for cross-language graph-based reuse of verification artefacts.
The paper presents a pipeline that converts imperative programs and their annotations into typed, attributed graphs, integrating AST parsing with semantic embeddings from models like SentenceTransformer and CodeBERT. Results show consistent graph representations across C, Java, and Dafny, enabling future verification artefact reuse.
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.