SemanticForge: Semantic-Aware Code Generation

• SemanticForge is a repository-level framework that leverages semantic knowledge graphs and constraint satisfaction techniques to minimize logical and schematic hallucinations in LLM-generated code.
• It employs a dual static–dynamic graph reconciliation process, neural query planning, and SMT-integrated beam search to enforce strict repository constraints during code generation.
• Its incremental knowledge graph maintenance and benchmark validations demonstrate significant improvements in pass rates and error reductions over conventional LLM-based synthesis methods.

SemanticForge is a repository-level code generation framework that integrates explicit semantic knowledge graphs and constraint satisfaction techniques to address systematic errors in LLM code synthesis. The approach introduces four interconnected algorithmic components designed to mitigate two critical failure modes in contemporary code generation: logical hallucination—where synthesized code passes compilation but implements incorrect control or data flow—and schematic hallucination—where generated code violates repository or language schema constraints. SemanticForge achieves formally guaranteed reductions in such errors by elevating semantic comprehension from prompt-level to repository-wide and by verifying structural and schematic constraints throughout code generation and maintenance.

1. Failure Modes in LLM-Based Code Generation

SemanticForge systematically addresses two dominant classes of code generation errors:

• Logical hallucination: Occurs when generated code compiles but mis-implements intended control or data flow. Formally, if $y$ is the generated program and $\operatorname{compile}(y) = \text{SUCCESS}$, then $\exists$ test $t$ such that $$\operatorname{execute}(t, y) \neq \operatorname{expected}(t)$$. Typical examples include incorrect loop bounds, missing state updates, and improper API call ordering.
• Schematic hallucination: Manifests as type mismatches, signature violations, missing imports, or unauthorized member access. Formally, for knowledge graph $\mathcal{G}$, if $$\mathcal{C}(y, \mathcal{G}) = \{c \in \text{constraints from } \mathcal{G}:\ \text{violates}(y,c)\}$$ is non-empty, then $y$ exhibits schematic hallucination.

Both error classes stem from the absence of explicit, queryable representations of repository-wide semantics, which lightweight retrieval-based or prompt-engineering methods cannot provide. By contrast, persistent repository knowledge graphs enable the extraction of precise control-flow, call-chain, type, and visibility constraints, facilitating in situ verification and thus reducing these failure rates.

2. Dual Static–Dynamic Knowledge Graph Reconciliation

Central to the SemanticForge pipeline is an algorithm for the automatic reconciliation of dual static–dynamic knowledge graphs:

• Key constructs: $\mathcal{G}_s$ (static analysis graph), $\mathcal{G}_d$ (dynamic trace graph), and $\mathcal{G}^*$ (ground-truth dependence graph). The objective is to produce $$\mathcal{G} = \text{merge}(\mathcal{G}_s, \mathcal{G}_d)$$ minimizing the structural Hamming distance $$d_{sh}(\mathcal{G}, \mathcal{G}^*) = |V \oplus V^*| + |E \oplus E^*|$$.
• Algorithmic steps:

1. Initialize $\mathcal{G}$ with the static graph $\mathcal{G}_s$.
2. For each dynamic edge $e$ in $\mathcal{G}_d$, if $e \notin \mathcal{G}$, insert $e$; otherwise, refine type or signature attributes at that edge.
3. At polymorphic call sites with ambiguous targets, update the static target if a concrete dynamic target is observed.
4. Return the unified graph $\mathcal{G}$.

• Correctness guarantee: As test coverage $c \to 100\%$, $\mathcal{G}$ converges to $\mathcal{G}^*$, since dynamic reconciliation only adds or refines edges, never deletes valid static approximations.

This dual-graph approach provides a refined semantic substrate for all downstream query and generation tasks, ensuring repository knowledge completeness as coverage increases.

3. Neural Query Planner for Structured Knowledge Graph Queries

SemanticForge leverages a neural-network-based planner to generate precise graph queries from natural language instructions:

• Architectural overview:
  • Encoder: Flan-T5 encodes the natural language instruction $u$.
  • Decoder: An autoregressive graph-query generator produces statements in a context-free grammar (e.g., $S \to \text{SELECT}\ \ldots\ \text{WHERE}\ \ldots$) constrained by the repository schema.
• Training regimen: REINFORCE policy gradient maximizes reward $$R(y, \mathcal{G}_u) = w_1\cdot\operatorname{TestPass}(y) + w_2 \cdot (1-\operatorname{TypeViolations}(y)) - w_3|\mathcal{G}_u|$$ with $w_1=1.0$, $w_2=0.3$, $w_3=0.001$; variance reduction is accomplished via advantage normalization, gradient clipping, and entropy bonus.
• Empirical results:
  • Context-selection precision: 73% versus 51% for BM25 retrieval.
  • Query planning complexity: $O(n\log n)$ versus $O(2^n)$ for exhaustive search.

Explicit query planning with schema constraints enables efficient extraction of relevant semantic subgraphs for code synthesis and strongly mitigates context dilution.

4. SMT-Integrated Beam Search for Real-Time Constraint Satisfaction

During code generation, SemanticForge performs real-time verification of output candidates via an SMT-augmented beam search:

• Objective: Maximization of $\log P_\theta(y|u, \mathcal{G}_u)$, subject to $\mathcal{C}(y, \mathcal{G}_u) = \emptyset$, i.e., only outputs free of schematic constraint violations are permitted.
• Constraint types and SMT encoding:
  • Type constraints: e.g., $$\operatorname{type\text{-}of}(\text{expr}) \subset \text{expected-type}$$.
  • Arity constraints: e.g., function arity matches number of arguments.
  • Visibility constraints: via uninterpreted predicates on variable accessibility.
  • Architectural patterns: custom predicates encode, for example, design-pattern adherence.
• Pseudocode:
  • Input beam width $k$, initial SMT state $S_0$.
  • Expand beams, at each step forming candidate $(y+t, S')$ only if $\operatorname{SMT.check}(S') = \text{SAT}$.
  • Retain top-$k$ completions.
• System properties:
  • Reuses solver contexts for incremental solving.
  • Utilizes batch-checking per beam expansion.
  • Achieves only a 6% latency overhead while eliminating 89% of schematic errors.

By integrating formal constraint verification directly in the generative decoding loop, SemanticForge enforces semantic soundness proactively, not reactively.

5. Incremental Knowledge Graph Maintenance

Efficiently updating repository-wide knowledge graphs in response to code changes is accomplished via an incremental algorithm:

• Update propagation:

1. Direct impact: Identify nodes whose AST instances changed.
2. Transitive impact: Compute transitive closure over actively dependent nodes (calls, imports).
3. Invalidation: Remove impacted subgraph from previous snapshot.
4. Re-extraction: Generate new subgraph for changed code via extraction function $F_E(\Delta R)$.
5. Merge: Re-route and merge, deferring resolution of cross-file or cross-module references lazily until they are queried.

• Complexity analysis:
  • Impact analysis: $\leq |\Delta R| \cdot d$ nodes ($d$ = maximal dependency depth).
  • Each graph operation: $O(\log n)$ in indexed structures.
  • Overall update: $O(|\Delta R| \cdot d \cdot \log n)$.
  • Amortized per-reference cost is $O(1)$ due to lazy resolution.
• Theoretical guarantee: After any update sequence, the incrementally maintained graph $\mathcal{G}_t^{inc}$ is semantically equivalent to a full rebuild $\mathcal{G}_t^{full}$.

This mechanism enables repository-scale semantic models to remain consistent under continuous development, with provably bounded overhead.

6. Evaluation, Case Study, and Empirical Impact

The effectiveness of SemanticForge is demonstrated via the RepoKG-50 benchmark, comprising 4,250 code generation tasks over 50 Python repositories (10K–500K LOC) with $>80\%$ test coverage.

• Key metrics and outcomes (test set):

| Metric | SemanticForge | Best Baseline (CodePlan) | |-------------------------------------------|--------------|-------------------------| | Pass@1 | 49.8% | 42.3% | | Schematic Hallucination Rate (SHR) | 14.7% | ~30% | | Logical Hallucination Rate (LHR) | 23.1% | ~36% | | Context Precision | 73% | 51% (BM25) | | Latency | 2.4 s | 5.7 s (plan) / 2.9 s (RAG) |

Ablation studies indicate a 7.3% Pass@1 drop upon removing dynamic traces, 8.9% drop (and doubling of SHR) with constraint removal, and 6.2% loss without neural planning. The approach enables 2.3$\times$ more transitive dependency coverage than retrieval baselines.

• Case paper—Cache eviction task:
  • Baseline LLM output exhibits logical hallucination by selecting the wrong cache key for eviction.
  • SemanticForge, via graph queries and SMT-guided selection, produces correct, test-passing code with zero signature errors.

Limitations include difficulty with tasks requiring algorithmic reasoning beyond schema or pattern enforcement, lack of mixed-language repository support, and dependence on test coverage quality for dynamic trace completeness.

7. Significance and Outlook

SemanticForge establishes an explicit, queryable substrate for repository-wide program semantics which is maintained and exploited throughout context selection, code generation, and maintenance. By architecting code synthesis workflows around persistent, formally reconcilable static–dynamic knowledge graphs and constraint-verified decoding, it realizes scalable, provably-sound reductions in both logical and schematic hallucinations. The approach demonstrates substantial empirical improvements over retrieval-augmented generation and neural planning baselines, indicating a paradigm shift toward semantically-aware, graph-centric code generation. A plausible implication is that integrating richer semantic representations and constraint solving might be essential for future LLM frameworks to achieve robust correctness at repository scale. Continued research directions may include tractable handling of mixed-language codebases and the incorporation of more advanced algorithmic reasoning modules.