[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85580-en":3,"doc-seo-85580-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85580,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","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. The paper presents a pipeline that converts imperative programs plus annotations into typed, attributed graphs. It parses abstract syntax trees and enriches graph nodes with semantic embeddings from SentenceTransformer and CodeBERT. Experiments across C with ACSL, Java with JML, and Dafny show consistent graph representations across languages and annotation styles, enabling scalable reuse via future enrichment and approximate matching.","Graph Construction and Matching for Imperative Programs using Neural and Structural Methods  \nArshad Beg1, *,†, Diarmuid O’Donoghue1,† and Rosemary Monahan1,† 1 Maynooth University, Co. Kildare, Ireland  \nAbstract  \nReusing 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.  \nKeywords  \nGraph Construction, Graph Matching, Language Syntax Tree Parsing, Large Language Models  \n1. Introduction  \nReusing verification artefacts such as specifications, contracts, and proofs remains a challenging task in software verification. Although large repositories of such artefacts exist, their reuse is still largely manual. Developers must search for relevant artefacts and adapt them to new contexts, even when similar solutions already exist. This process is difficult because artefacts often differ in syntax, abstraction level, and domain-specific vocabulary. In our long-term research vision [1], we identified reuse as a key problem that requires principled mechanisms for discovering semantic correspondences between artefacts. Rather than generating new artefacts, the focus is on identifying structural and semantic similarities across existing ones. This can be understood as a problem of semantic matching under partial equivalence, where only fragments of behaviour or intent may align across different implementations. To address this, we proposed representing verification artefacts as typed, attributed graphs [1] . In this representation, nodes capture semantic elements such as variables, predicates, transitions, and proof obligations, while edges encode relationships such as control-flow, dataflow, and logical dependencies. This abstraction allows programs, specifications, and proofs to be treated uniformly, enabling comparison across heterogeneous artefacts. However, graph structure alone is not sufficient. Important semantic information is also embedded in identifiers, comments, and logical expressions.  \nTo capture this, large language models (LLMs) can be used to generate embeddings that enrich graph nodes with semantic information. These embeddings allow the system to identify similarities that are not visible at the structural level.  \nIn this paper, we focus on the first stage of this broader vision: graph construction for verification artefacts. We implement and evaluate a pipeline that translates imperative programs into typed, attributed graphs, forming the foundation for subsequent semantic enrichment and matching. The overall workflow, illustrated in Figure 1, outlines how graph construction integrates with LLM-based enrichment and approximate graph matching to support artefact reuse.  \nKey Contributions. We list down the key contributions:  \n• We present a unified, end-to-end pipeline for graph construction and matching of imperative programs across multiple languages, including C, Java, and C\\#, along with their specification  \nSci-K 2026 – 6th International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment 26 October 2026 -  \nFigure 1: Workflow for verification artefact reuse via hybrid graph matching and LLM-based semantic enric","cbCaiuAyyccGSUJr","https://ap.wps.com/l/cbCaiuAyyccGSUJr","pdf",1282827,2,1,14,"English","en",105,"# 1. Introduction\n# 2. Syntactical Differences of Selected Languages\n# 3. Related Work\n# 4. Experimental Setup\n# 5. Implementation of Graph Construction and Matching\n# 6. 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