一句话：提出神经符号软件工程（NSE）范式，将神经学习与符号规则推理结合，并引入受控随机性以提升效率、可靠性与透明度。
大型代码模型（LCMs）显著推动了代码生成、文档编写、代码审查与缺陷修复等软件工程自动化任务，但这种进步伴随代价：高性能往往意味着指数级的计算开销、可解释性下降，以及对数百亿参数级数据密集型模型的日益依赖。本文提出「神经符号软件工程」（NSE）这一新范式，主张将神经学习与符号（规则驱动）推理相结合，并策略性地引入受控的「混沌」来源，用以模拟真实软件系统的复杂动态。该混合方法旨在提升 AI 驱动软件工程的效率、可靠性与透明度，同时借助受控随机性适应不断变化的需求、不可预测的系统行为与非确定性执行环境。作为一篇立场/愿景论文，NSE 通过重新定义 AI 驱动软件工程自动化的核心原则，为更具适应性、更透明、更契合现代软件开发实践的解决方案奠定基础，但文中未给出具体实证评估。
The emergence of Large Code Models (LCMs) has transformed software engineering (SE) automation, driving significant advancements in tasks such as code generation, source code documentation, code review, and bug fixing. However, these advancements come with trade-offs: achieving high performance often entails exponential computational costs, reduced interpretability, and an increasing dependence on data-intensive models with hundreds of billions of parameters. In this paper, we propose Neurosymbolic Software Engineering, in short NSE, as a promising paradigm combining neural learning with symbolic (rule-based) reasoning, while strategically introducing a controlled source of chaos to simulate the complex dynamics of real-world software systems. This hybrid methodology aims to enhance efficiency, reliability, and transparency in AI-driven software engineering, while introducing controlled randomness to adapt to evolving requirements, unpredictable system behaviors, and non-deterministic execution environments. By redefining the core principles of AI-driven software engineering automation, NSE lays the groundwork for solutions that are more adaptable, transparent, and closely aligned with the evolving demands of modern software development practices.