➀ The paper proposes a neurosymbolic approach (NSG) combining neural networks with symbolic static analysis to improve automated Java code generation, achieving 86% syntax correctness and 40% code similarity without relying on large language models;
➀ Cadence experts highlight how NSG integrates compiler-like symbol tables during training, addressing weaknesses of LLMs in generating semantically valid code;
➁ The method shows promise for semiconductor verification by enforcing strict language rules in EDA tools, outperforming GPT-3 and CodeGPT in code accuracy.