Recent advances in large language models (LLMs) have demonstrated strong capabilities in software engineering tasks, raising expectations of revolutionary productivity gains. However, enterprise software development is largely driven by incremental evolution, where challenges extend far beyond routine coding and depend critically on tacit knowledge, including design decisions at different levels and historical trade-offs. To achieve effective AI-powered support for complex software development, we should align emerging AI capabilities with the practical realities of enterprise development. To this end, we systematically identify challenges from both software and LLM perspectives. Alongside these challenges, we outline opportunities where AI and structured knowledge frameworks can enhance decision-making in tasks such as issue localization and impact analysis. To address these needs, we propose the Code Digital Twin, a living framework that models both the physical and conceptual layers of software, preserves tacit knowledge, and co-evolves with the codebase. By integrating hybrid knowledge representations, multi-stage extraction pipelines, incremental updates, LLM-empowered applications, and human-in-the-loop feedback, the Code Digital Twin transforms fragmented knowledge into explicit and actionable representations. Our vision positions it as a bridge between AI advancements and enterprise software realities, providing a concrete roadmap toward sustainable, intelligent, and resilient development and evolution of ultra-complex systems.
翻译：近年来，大型语言模型（LLMs）在软件工程任务中展现出强大能力，引发了人们对革命性生产力提升的期待。然而，企业级软件开发在很大程度上由增量式演进驱动，其挑战远超出常规编码范畴，并高度依赖于隐性知识，包括不同层级的设计决策与历史权衡。为实现对复杂软件开发的有效AI赋能支持，我们应将新兴AI能力与企业开发的实际现状相结合。为此，我们系统性地从软件工程与LLMs双重视角识别关键挑战。基于这些挑战，我们进一步勾勒出AI与结构化知识框架可提升决策能力的机遇领域，例如问题定位与影响分析等任务。为应对这些需求，本文提出代码数字孪生——一个对软件的物理层与概念层进行建模、保存隐性知识并与代码库协同演进的动态框架。通过融合混合知识表示、多阶段提取流水线、增量更新机制、LLM赋能应用以及人在回路的反馈机制，代码数字孪生将碎片化知识转化为显式且可操作的表征。我们的愿景是将其定位为AI进展与企业软件现实之间的桥梁，为超复杂系统的可持续、智能化与高韧性开发演进提供具体实施路径。
相关内容
微信扫码咨询专知VIP会员