SemanticForge: Repository-Level Code Generation through Semantic Knowledge Graphs and Constraint Satisfaction

• URL: http://arxiv.org/abs/2511.07584v1
• Date: Wed, 12 Nov 2025 01:05:36 GMT
• Title: SemanticForge: Repository-Level Code Generation through Semantic Knowledge Graphs and Constraint Satisfaction
• Authors: Wuyang Zhang, Chenkai Zhang, Zhen Luo, Jianming Ma, Wangming Yuan, Chuqiao Gu, Chenwei Feng,
• Abstract summary: Large language models (LLMs) have transformed software development by enabling automated code generation, yet they frequently suffer from systematic errors that limit practical deployment.<n>We identify two critical failure modes: textitlogical hallucination (incorrect control/data-flow reasoning) and textitschematic hallucination (type mismatches, signature violations, and architectural inconsistencies).<n>This paper presents textbfSemanticForge, which introduces four fundamental algorithmic advances for semantically-aware code generation.
• Score: 7.46733617565624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have transformed software development by enabling automated code generation, yet they frequently suffer from systematic errors that limit practical deployment. We identify two critical failure modes: \textit{logical hallucination} (incorrect control/data-flow reasoning) and \textit{schematic hallucination} (type mismatches, signature violations, and architectural inconsistencies). These errors stem from the absence of explicit, queryable representations of repository-wide semantics. This paper presents \textbf{SemanticForge}, which introduces four fundamental algorithmic advances for semantically-aware code generation: (1) a novel automatic reconciliation algorithm for dual static-dynamic knowledge graphs, unifying compile-time and runtime program semantics; (2) a neural approach that learns to generate structured graph queries from natural language, achieving 73\% precision versus 51\% for traditional retrieval; (3) a novel beam search algorithm with integrated SMT solving, enabling real-time constraint verification during generation rather than post-hoc validation; and (4) an incremental maintenance algorithm that updates knowledge graphs in $O(|ΔR| \cdot \log n)$ time while maintaining semantic equivalence.

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