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

arXiv:2511.07584v1 Announce Type: cross
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: textitlogical hallucination (incorrect control/data-flow reasoning) and textitschematic 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 textbfSemanticForge, 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 $O(|Delta R| cdot log n)$ time while maintaining semantic equivalence.