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Neural Program Generation Modulo Static Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Condition method-body generation on static-analysis attributes | NSG versus CodeGPT · Java method generation | Pass all checks: 67.73% -> 86.41% | Helped |
| Condition method-body generation on static-analysis attributes (ablation) | NSG versus CNG · Java method generation | Pass all checks: 12.87% -> 86.41% | Helped |
| Condition method-body generation on static-analysis attributes | NSG versus GPTNeo1.3B · Java method generation | Set of API Calls: 37% -> 53% | Helped |
| Condition method-body generation on static-analysis attributes (ablation) | NSG versus CNG · Java method generation | Sequences of Program Paths: 17% -> 39% | Helped |
| Condition method-body generation on static-analysis attributes | NSG versus GPTNeo1.3B · Java method generation | AST Exact Match: 15% -> 26% | Helped |
| Condition method-body generation on static-analysis attributes | NSG versus Codex · Java method generation | No type errors: 91.42% -> 97.08% | Helped |
Closest papers by meaning
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- Neuro-Symbolic Program Synthesis (ICLR 2017 · similarity 0.86)
- Gradient-Based Program Synthesis with Neurally Interpreted Languages (ICLR 2026 · similarity 0.86)
- Generative Code Modeling with Graphs (ICLR 2019 · similarity 0.85)
- Execution Guided Line-by-Line Code Generation (NeurIPS 2025 · similarity 0.84)
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