Graph Construction and Matching for Imperative Programs using Neural and Structural Methods

[Submitted on 29 Apr 2026 (v1), last revised 12 Jul 2026 (this version, v3)]

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Abstract: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 programs. 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.

Submission history

From: Arshad Beg [view email]
[v1] Wed, 29 Apr 2026 11:59:53 UTC (794 KB)
[v2] Fri, 15 May 2026 15:29:14 UTC (794 KB)
[v3] Sun, 12 Jul 2026 12:26:53 UTC (784 KB)